Assessing High House Prices: Bubbles, Fundamentals and ... · markets can appear ÒexuberantÓ even when houses are in fact reasonably priced. We construct a measure for evaluating

Assessing High House Prices BubblesFundamentals and Misperceptions

Charles Himmelberg Christopher Mayer andTodd Sinai

H ouse price watching has become a national pastime By 2004 68 percentof households owned their own homes and for most of them housingequity will make up nearly all of their nonpension assets at retirement

(Venti and Wise 1991) Many of the 32 percent who rent are younger householdsowners-in-waiting who watch housing markets with great interest (or concern) Thepreoccupation with housing markets has been particularly strong of late becauserecent house price growth has been rampant especially in certain cities Between1975 and 1995 real single-family house prices in the United States increased anaverage of 05 percent per year or 10 percent over the course of two decades Bycontrast from 1995 to 2004 national real house prices grew 36 percent per yeara more than seven-fold increase in the annual rate of real appreciation and totalingnearly 40 percent in one decade In some individual cities such as San Franciscoand Boston real home prices grew about 75 percent from 1995 to 2004 almostdouble the national average

How does one tell when rapid growth in house prices is caused by fundamentalfactors of supply and demand and when it is an unsustainable bubble Stiglitz(1990) provided a general definition of asset bubbles in this journal ldquo[I]f thereason that the price is high today is only because investors believe that the sellingprice is high tomorrowmdashwhen lsquofundamentalrsquo factors do not seem to justify such a

y Charles Himmelberg is Senior Economist Federal Reserve Bank of New York New YorkNew York Christopher Mayer is Paul Milstein Professor Finance and Economics ColumbiaBusiness School Columbia University New York New York Todd Sinai is Associate Professorof Real Estate Wharton School University of Pennsylvania Philadelphia PennsylvaniaMayer is also Research Associate and is a Sinai Faculty Research Fellow NationalBureau of Economic Research Cambridge Massachusetts Their e-mail addresses areCharlesHimmelbergnyfrborg cm310columbiaedu and sinaiwhartonupennedurespectively

Journal of Economic PerspectivesmdashVolume 19 Number 4mdashFall 2005mdashPages 67ndash92

pricemdashthen a bubble exists At least in the short run the high price of the asset ismerited because it yields a return (capital gain plus dividend) equal to that onalternative assetsrdquo The ldquodividendrdquo portion of the return from owning a housecomes from the rent the owner saves by living in the house rent-free and the capitalgain comes from house price appreciation over time We think of a housing bubbleas being driven by homebuyers who are willing to pay inflated prices for housestoday because they expect unrealistically high housing appreciation in the future1

In this paper we explain how to assess the state of house pricesmdashboth whetherthere is a bubble and what underlying factors support housing demandmdashin a waythat is grounded in economic theory In doing so we correct four common fallaciesabout the costliness of the housing market First the price of a house is not thesame as the annual cost of owning so it does not necessarily follow from risingprices of houses that ownership is becoming more expensive Second high pricegrowth is not evidence per se that housing is overvalued In some local housingmarkets house price growth can exceed the national average rate of appreciationfor very long periods of time Third differences in expected appreciation rates andtaxes can lead to considerable variability in the price-to-rent ratio across marketsFinally the sensitivity of house prices to changes in fundamentals is higher at timeswhen real long-term interest rates are already low and in cities where expectedprice growth is high so accelerating house price growth and outsized price in-creases in certain markets are not intrinsically signs of a bubble For all of the abovereasons conventional metrics for assessing pricing in the housing market such asprice-to-rent ratios or price-to-income ratios generally fail to reflect accurately thestate of housing costs To the eyes of analysts employing such measures housingmarkets can appear ldquoexuberantrdquo even when houses are in fact reasonably pricedWe construct a measure for evaluating the cost of home owning that is standard foreconomistsmdashthe imputed annual rental cost of owning a home a variant of theuser cost of housingmdashand apply it to 25 years of history across a wide variety ofhousing markets This calculation enables us to estimate the time pattern ofhousing costs within a market Given the relatively short time series it is hard to sayjust how costly a market must become before it is unsustainably expensive However wecan determine how expensive a market is currently relative to its own history

As of the end of 2004 our analysis reveals little evidence of a housing bubbleIn high-appreciation markets like San Francisco Boston and New York currenthousing prices are not cheap but our calculations do not reveal large priceincreases in excess of fundamentals For such cities expectations of outsized capitalgains appear to play at best a very small role in single-family house prices Ratherrecent price growth is supported by basic economic factors such as low reallong-term interest rates high income growth and housing price levels that hadfallen to unusually low levels during the mid-1990s The growth in price-to-rentratiosmdashespecially in cities where this ratio was already highmdashcan be explained bythe fact that house prices are more sensitive to real long-term interest rates when

1 Case and Shiller (2004) also use this definition

68 Journal of Economic Perspectives

interest rates are already low and even more sensitive in cities where house price growthis typically high During the late 1980s our metrics indicate that house prices in manycities were in fact overvalued (for example Boston Los Angeles New York and SanFrancisco) and prices in these cities subsequently fell Thus we do not find thathousing prices are always close to equilibrium levels Still in 2004 prices lookedreasonable Only a few cities such as Miami Fort Lauderdale Portland (Oregon) andto a degree San Diego had valuation ratios approaching those of the 1980s

Of course just because the data do not indicate bubbles in most cities in 2004does not mean that prices cannot fall Deterioration in underlying economicfundamentals such as an unexpected future rise in real long-term interest rates ora decline in economic growth could easily cause a fall in house prices Indeedbecause real long-term interest rates are currently so low our calculations suggestthat housing costs are more sensitive to changes in real long-term interest rates nowthan any other time in the last 25 years Finally our data do not cover and hencedo not allow us to comment on the condominium market which due to its lowertransaction costs and higher liquidity may be more vulnerable to overvaluation andoverbuilding arising from investor speculation

How Not to Judge the Sustainability of Housing Prices

To assess whether house prices are unsustainably high casual observers oftenbegin by looking at house price growth Figure 1 shows that between 1980 and2004 real house prices at the national level grew 39 percent or 14 percentannually To compute these numbers we use data from the Office of FederalHousing Enterprise Oversight (OFHEO) which produces the most widely quotedsingle-family house price index The OFHEO price index is based on multipleobservations of the price of a given house so unlike a conventional price index itcontrols to some extent for the changing quality in the mix of houses sold at anygiven time although changes in quality due to home renovations will not benormalized using this index These housing price data are available for over100 metropolitan areas since 1980 An index cannot be used to compare pricelevels across cities but it can be used to calculate growth rates and to compareprices over time The major drawbacks of these data are that they are subject tobiases due to changes in the quality of existing houses and they cover only thosehouses with so-called ldquoconventionalrdquo mortgages that have been purchased byFannie Mae and Freddie Mac Since the current maximum lending limit for suchmortgages in 2005 is $359650 for states in the continental United States high-priced houses are underrepresented in the OFHEO index2

2 This is a potentially important caveat because markets for high-priced houses may behave differentlyMayer (1993) for example shows that the prices of high-priced houses are more volatile than the valuesof lower-priced properties The National Association of Realtors (NAR) produces an index that is notsubject to the cap but which does not control for changes in the mix of houses that sell over time Ourconclusions are unchanged if we use the NAR index for our analysis instead of the OFHEO index

Charles Himmelberg Christopher Mayer and Todd Sinai 69

Over the last quarter century run-ups in house prices are common but so aresubsequent declines The national average real house price fell by 72 percent from1980 to 1982 rose by 162 percent from 1982 to 1989 fell by 8 percent from 1989to 1995 and then rose by 40 percent from 1995 to 2004 About one-third of theincrease in real housing prices since 1995 reflects the return of house prices to theirprevious real peak of 1989 Housing prices in 2004 have since risen to levels29 percent over the previous 1989 peak Of course these summary descriptions andgraphs of prices do not reveal whether fundamental factors or a price bubble areat work in the first half of the 2000s we shall return to this question

The national average masks considerable differences across metropolitan sta-tistical areas Table 1 presents index values that are standardized so that a value of10 corresponds to the average price level in a metropolitan statistical area over the1980ndash2004 period We include all 46 metropolitan areas with available data onhouse price changes rents and per capita income for the comparisons that followOver that time period metro area house prices have followed one of three patternsIn about half of the cities including Boston New York and San Francisco houseprices peaked in the late 1980s fell to a trough in the 1990s and rebounded by2004 Prices in 18 more metropolitan areas including Miami and Denver have aldquoUrdquo-shaped history high in the early 1980s and high again by the end of the sampleA few cities such as Houston and New Orleans follow a third pattern real houseprices have declined since 1980 and have not fully recovered The first threecolumns of Table 1 report the value of the house price index at three points intime the most recent house price peak the subsequent trough and as of 2004

Of the cities in the first category a few have exhibited extraordinary appreci-ation since their mid-1990s trough For example by 2004 single-family house prices

Figure 1Real US House Price Index Price-to-Rent and Price-to-Income Ratios(ratios normalized to their 25-year average)

PriceIncome

Real price

PriceRent

1980

8

1

12

14

1985 1990 1995 2000 2005

Source OFHEO Price Index REIS Inc BEA BLS CPI Index-All Urban Consumers


Table 1Conventional Measures of House Price Dynamics

Real price index Price-to-rent ratio Price-to-income ratio

Atpriorpeak

At priortrough

In2004

Atpriorpeak

At priortrough

In2004

Atpriorpeak

At priortrough

In2004

Markets where house prices peaked in the late 1980s and had a trough in the 1990sAtlanta GA 104 091 120 104 089 115 122 087 107Austin TX 123 092 113 142 086 105 131 078 094Baltimore MD 108 096 141 103 097 125 111 086 117Boston MA 121 086 167 119 090 136 128 085 136Dallas TX 123 084 099 126 085 096 138 077 088District of Columbia DC 114 092 150 111 096 131 112 085 127Jacksonville FL 105 089 134 106 090 125 122 084 116Los Angeles CA 127 083 159 121 098 141 123 084 148Nashville TN 104 089 117 105 095 114 124 092 095New York NY 126 091 154 136 082 119 129 085 134Norfolk VA 108 094 130 109 097 116 116 088 114Oakland CA 111 085 168 114 087 151 117 084 138Orange County CA 120 082 168 118 090 149 118 083 156Philadelphia PA 118 095 136 114 097 125 119 086 114Phoenix AZ 109 084 125 104 091 125 123 089 112Portland OR 124 123 140 106 072 141 118 104 125Raleigh-Durham NC 105 093 111 111 096 108 126 088 098Richmond VA 104 096 122 104 095 115 123 090 108Sacramento CA 116 086 163 120 088 134 120 083 147San Bernadino-Riverside CA 119 080 155 113 090 137 116 084 152San Diego CA 111 084 183 112 090 152 114 084 155San Francisco CA 115 087 165 126 082 144 121 087 137San Jose CA 111 086 162 122 081 162 121 087 132Seattle WA 104 101 145 104 095 135 108 099 123

Markets where house prices were high in the early 1980s and rebounded in the 2000sCharlotte NC 102 095 114 105 097 110 128 089 097Chicago IL 100 100 137 104 098 129 106 094 123Cincinnati OH 106 095 117 114 090 114 130 092 098Cleveland OH 104 086 118 109 087 120 123 097 106Columbus OH 099 095 118 103 095 119 124 092 100Denver CO 100 075 139 101 087 122 121 093 117Detroit MI 100 072 139 098 070 142 121 091 119Fort Lauderdale FL 105 088 151 111 086 143 125 084 140Indianapolis IN 103 097 110 103 103 117 127 088 093Kansas City KS 116 087 118 128 092 117 139 087 102Memphis TN 106 103 106 110 095 101 132 086 086Miami FL 105 094 158 099 098 145 124 091 146Milwaukee MN 107 085 132 110 082 136 125 095 113Minneapolis MN 102 087 148 100 097 145 127 086 125Orlando FL 105 089 126 113 088 118 123 084 113Pittsburgh PA 107 103 117 106 104 120 126 088 098St Louis MO 108 092 122 101 099 123 133 088 105Tampa FL 104 088 138 104 090 128 122 083 122

Markets where house prices have declined since the early 1980s and never fully reboundedFort Worth TX 123 085 097 125 085 095 145 075 086Houston TX 138 082 102 130 084 096 150 076 087New Orleans LA 124 080 116 108 086 119 142 089 096San Antonio TX 130 083 097 140 083 095 147 072 081

Notes Twenty-five-year within-MSA average is normalized to 100 Values are not comparable acrossmarkets 2004 figures are in bold when they exceed their prior peak values

in Boston and seven California cities had risen more than 85 percent (adjusted forinflation) with San Diego prices going up almost 120 percent to a level 83 percentabove their 25-year average Much of this increase merely offset prior house pricedeclines which were as much as 34 percent in Los Angeles and 25 percent or morein seven other cities (from peak to trough) But even relative to their prior peakhouse prices most of these cities (excluding the two in Texas) have appreciatedconsiderably House prices in San Diego Boston and New York for example wereup 83 38 and 22 percent above their respective prior peaks3

Real house prices in the middle groupmdashfor example Austin CincinnatiCleveland Memphis Kansas City Pittsburgh and St Louismdashall exceeded their25-year average by 2004 with Miami and Fort Lauderdale more than 50 percentabove average Real housing values in a few markets even declined as in Dallas FortWorth Houston New Orleans and San Antonio with real annual house prices inHouston falling 12 percent per year on average4

A second commonly cited measure used to assess housing valuations is thehouse price-to-rent ratio which is akin to a price-to-earnings multiple for stocksThis metric is intended to reflect the relative cost of owning versus rentingIntuitively when house prices are too high relative to rents potential homebuyerswill choose instead to rent thus reducing the demand for houses and bringinghouse prices back into line with rents A common argument is that when price-to-rent ratios remain high for a prolonged period it must be that prices are beingsustained by unrealistic expectations of future price gains rather than the funda-mental rental value and hence contain a ldquobubblerdquo

We obtained data on metro area rents between 1980 and 2004 from REIS areal estate consulting firm REIS provides annual average rents for a ldquorepresenta-tiverdquo two-bedroom apartment in each metro area listed in Table 1 As with theOFHEO house price indexes the rent data attempt to hold the quality of the rentalunit constant over time Again we cannot compare rent levels across marketsbecause our rent indices are not standardized to the same representative apartmentacross markets We can however construct a metro area index of real house pricesrelative to real rents and compare movements in this index over time As before westandardize the price-to-rent index so that a value of 10 corresponds to the averagevalue over the 1980ndash2004 period

Figure 1 shows that the price-to-rent ratio roughly tracks the overall houseprice movement After 1989 the price-to-rent ratio slowly declined reaching atrough in 2000 In the succeeding four years the price-to-rent ratio grew 27 per-cent leaving the ratio 15 percent above its previous peak (in 1989) By way ofcomparison the prior trough-to-peak swing from 1985 to 1989 was less thanone-half the growth in the price-to-rent ratio from 2000ndash2004 The 1989 peak

3 House prices in these cities appear more volatile in booms and busts a point emphasized by Case andShiller (2004) We discuss this point in more detail below4 In an on-line appendix appended to this article at the website httpwwwe-jeporg Figure 1 plotshouse price indexes for all 46 metropolitan statistical areas in our data


occurred right before a housing price decline lending weight to the view that theprice-to-rent ratio is an indicator of overheating in the housing market

At the metropolitan statistical area level many of the markets with a highgrowth rate of price-to-rent since their prior trough are the same markets that sawlarge price appreciation (for example cities in California and the northeast)5 SanJose and Detroit experienced a doubling of their price-to-rent ratios since theirprior troughs and Portland Oregon experienced 95 percent growth

More striking is the fact that the price-to-rent ratio in 2004 exceeds the 25-yearaverage everywhere except for four cities in Texas and surpasses the prior peak in80 percent of the cities Column 6 (Table 1) shows the ratio of price-to-rent in 2004relative to its average value from 1980ndash2004 In three of those cities the price-to-rent ratio in 2004 exceeds its average value by more than 50 percent San Franciscois 14 percent above its prior peak and New York is 12 percent below

A third measure that is commonly used to assess whether housing pricesare ldquotoo highrdquo is the price-to-income ratio Unlike the price-to-rent ratio whichmeasures the relative cost of owning and renting the price-to-income ratio providesa measure of local housing costs relative to the local ability to pay

Figure 1 shows the price-to-income ratio computed as the OFHEO price indexdivided by an index of mean per capita income based on data from the US Bureauof Economic Analysis At the national level this ratio declined in the early 1980spartially recovered to peak in 1987 and then declined again over the next decadebottoming out 23 percent below its 1980 level After 1998 the price-to-income ratiobegan to rise again and by 2003 had exceeded its 1988 peak Despite growing21 percent over the last six years it has not yet returned to its 1980 level Thedecline in this ratio during most of the 1990s reveals that the growth in real houseprices nationally was outpaced by the high growth in incomes (McCarthy andPeach 2004)

At the metro area level as shown in the last three columns in Table 1price-to-income ratios have generally increased the most in cities where house pricegrowth has been highest Price-to-income is above its peak value in many of thesecities (though not by nearly as much as the price-to-rent ratio) and as much as40ndash50 percent above its 25-year average (the last column of Table 1) Yet there isa lot of heterogeneity in this measure of house price excess In Houston Dallas andother southern cities price-to-income is well below its 25-year average

If high growth rates of house prices price-to-rent ratios and price-to-incomeratios were reliable indicators of a rising cost of obtaining housing then theserecent trends would indeed provide reasons to suspect overvaluation in manyhousing markets However as this paper will explain these measures are inade-quate to assess whether the housing market is the grip of a speculative bubble

5 A few other markets such as Miami Milwaukee Minneapolis and Portland saw their price-to-rent ratiorise by more than 50 percent These all are markets that experienced economic declines in the 1970sand 1980s but have recovered appreciably since then


The User Cost of Housing

The key mistake committed by the conventional measures of overheating inhousing markets is that they erroneously treat the purchase price of a house as if itwere the same as the annual cost of owning But consider purchasing a house for$1 million The cost of living in that house for one year is not $1 million Nor is thefinancial return on the house equal to just the capital gain or loss on that propertyA correct calculation of the financial return associated with an owner-occupiedproperty compares the value of living in that property for a yearmdashthe ldquoimputedrentrdquo or what it would have cost to rent an equivalent propertymdashwith the lostincome that one would have received if the owner had invested the capital in analternative investmentmdashthe ldquoopportunity cost of capitalrdquo This comparison shouldtake into account differences in risk tax benefits from owner-occupancy propertytaxes maintenance expenses and any anticipated capital gains from owning the home

This line of reasoning is the basis for an economically justified way of evalu-ating whether the level of housing prices is ldquotoo highrdquo or ldquotoo lowrdquo We calculate thetrue one-year cost of owning a house (the ldquouser costrdquo) which can then be comparedto rental costs or income levels to judge whether the cost of owning is out of linewith the cost of renting or unaffordable at local income levels This section reviewsthe most commonly used procedure for calculating the annual cost of homeown-ership discusses its implications for house prices and then reviews the assumptionsthat enter the calculation In this framework a house price bubble occurs whenhomeowners have unreasonably high expectations about future capital gains lead-ing them to perceive their user cost to be lower than it actually is and thus pay ldquotoomuchrdquo to purchase a house today

A FormulaThe formula for the annual cost of homeownership also known in the housing

literature as the ldquoimputed rentrdquo is the sum of six components representing bothcosts and offsetting benefits (Hendershott and Slemrod 1983 Poterba 1984)6 Thefirst component is the cost of foregone interest that the homeowner could haveearned by investing in something other than a house This one-year cost is calcu-lated as the price of housing Pt times the risk-free interest rate rt

rf The secondcomponent is the one-year cost of property taxes calculated as house price timesthe property tax rate t The third component is actually an offsetting benefit toowning namely the tax deductibility of mortgage interest and property taxes forfilers who itemize on their federal income taxes7 This can be estimated as the

6 These items should be viewed in opportunity cost terms For example an owner might make annualmaintenance expenditures or else allow his home to depreciate slowly in value either way a cost isincurred7 We use the mortgage rate when computing the tax benefit of owning a house as opposed to theTreasury rate used for the opportunity cost of capital Mortgage rates are typically 1ndash2 percent aboverisk-free rates of equivalent duration because borrowers typically have the options to ldquorefinancerdquo ifinterest rates go down and to default on the mortgage if property prices fall Homeowners can deductfrom their taxable income the additional expense associated with both of these options but they still


effective tax rate on income times the estimated mortgage and property taxpayments Ptt(rt

m t)8 The fourth term reflects maintenance costs expressed asa fraction t of home value Finally the fifth term gt1 is the expected capital gain(or loss) during the year and the sixth term Ptt represents an additional riskpremium to compensate homeowners for the higher risk of owning versus rentingThe sum of these six components gives the total annual cost of homeownership

Annual Cost of Ownership Pt rtrf Ptt Pt t rt

m t Pt t Pt gt1 Pt t

Equilibrium in the housing market implies that the expected annual cost ofowning a house should not exceed the annual cost of renting If annual ownershipcosts rise without a commensurate increase in rents house prices must fall toconvince potential homebuyers to buy instead of renting The converse happens ifannual ownership costs fall This naturally correcting process implies a ldquono arbi-tragerdquo condition that states that the one-year rent must equal the sum of the annualcosts of owning9 Using the above equation we can summarize this logic byequating annual rent with the annual cost of ownership We can rearrange theannual cost of ownership by moving the price term in front of everything else onthe right-hand side to get

Rt Ptut

where the fraction ut is known as the user cost of housing defined as

ut rtrf t trt

m t t gt1 t

The user cost ut is just a restatement of the annual total cost of ownership definedabove but expressed in terms of the cost per dollar of house value Expressing theuser cost in this way is particularly useful because rearranging the equation Rt Ptut as PtRt 1ut allows us to see that the equilibrium price-to-rent ratio shouldequal the inverse of the user cost Thus fluctuations in user costs (caused for

receive their financial benefits Thus the US government subsidizes the purchase of mortgages withprepayment and default options8 While it is widely recognized that mortgage payments are tax deductible the equity-financed portionof a house is usually tax-subsidized as well Owners do not pay income taxes on imputed rent (the moneythey ldquopay themselvesrdquo as owners of a property) or capital gains taxes for all but the largest gains Undercurrent tax law gains from the sale of owner-occupied property (first and second homes) are free oftaxation as long as the gain does not exceed $250000 for an individual or $500000 for a married coupleand the owner has lived in the house two of the last five years For a deeper analysis of the tax subsidyto owner-occupied housing see Hendershott and Slemrod (1983) and Gyourko and Sinai (2003)9 Poterba (1984) writes the formula for the imputed rental value of housing as

Rit Pit 1 it

1 rt itEitPit1 1 ititPit it rtPit

The rental formula in the text is an approximation of this formula

Assessing High House Prices Bubbles Fundamentals and Misperceptions 75

example by changes in interest rates and taxes) lead to predictable changes in theprice-to-rent ratio that reflect fundamentals not bubbles Comparing price-to-rentratios over time without considering changes in user costs is obviously misleading

An IllustrationFor the purpose of illustrating how the user cost model works suppose the

following i) the risk-free 10-year interest rate rrf is 45 percent ii) the mortgage raterm is 55 percent iii) the annual depreciation rate is 25 percent (HardingRosenthal and Sirmans 2004) iv) the marginal tax rate of the typical homebuyeris 25 percent v) the property tax rate is 15 percent vi) the risk premiumis 20 percent10 and vii) the long-run appreciation rate of housing prices is38 percent (expected inflation of 20 percent plus a real expected appreciationrate of housing of 18 percent the average from 1980ndash2004 for the metro areas inour sample for this paper)

Under these assumptions the predicted user cost is 50 percent that is forevery dollar of price the owner pays 5 cents per year in cost Leaving aside otherdifferences between renting and owning people should be willing to pay up to20 times (1005) the market rent to purchase a house11 Hence for example atwo-bedroom apartment that rents for $1000month ($12000year) should sellfor up to $240000 This price-to-rent ratio provides a baseline against whichhousing prices can be judged ldquotoo highrdquo or ldquotoo lowrdquo If price multiplied by the usercost exceeds the market rent housing is relatively costly

House Prices are More Sensitive to Changes in Real Interest Rates When Ratesare Already Low

The real interest rate is a key determinant of the user cost of housing (Belowwe explain why real long-term rather than short-term interest rates are especiallyimportant) A lower real interest rate reduces the user cost because the cost of debtfinancing is lower as is the opportunity cost of investing equity in a house Inpractical terms when the real interest rate is low homeownership is relativelyattractive because mortgage payments are low and alternative investments do notyield much Given that mortgage interest is tax deductible and the opportunity costof the equity in the house is a taxable return a percentage point decline in the realinterest rate reduces the user cost by 1 In our example dropping the 10-yearrate and mortgage rates from 45 and 55 percent to 4 and 5 percent respectivelywould cause the user cost to drop from 5 to 46 percent and cause the maximumprice-to-rent multiple to rise from 20 to 219

Similarly raising the income tax rate lowers the user cost of housing (Poterba

10 We obtain this estimated risk premium from Flavin and Yamashita (2002) This risk premium may betoo high because it ignores important factors such as the insurance value of owning a house in hedgingrisk associated with future changes in rents (Sinai and Souleles 2005) However choosing alternativevalues has little effect on the time series behavior of user costs which is the focus of this paper A moregeneral model than ours would allow the relative risk of owning versus renting to vary over time11 They may not have to pay that amount If housing is elastically supplied and price is above the costof construction builders will create more housing at a lower multiple to rents


1990) making housing less expensive than renting In the above example chang-ing the assumed income tax rate from 25 percent to 35 percent in the base caseexample raises the sustainable price-to-rent ratio from 20 to 23512 The reason isthat higher income taxes make the tax subsidy to owner-occupied housing worthmore which lowers the cost of housing relative to other goods

It is apparent from these calculations that the lower the user cost the higherthe sensitivity of the rent multiple to changes in interest rates or the tax subsidy Forexample suppose real interest rates fall by one percentage point or 075 percentafter taxes In a typical metropolitan area today with a 5 percent user cost the usercost falls to 425 and the price-to-rent ratio could rise from 20 to 238 an increaseof 19 percent If the user cost were 7 percent as it was at times during the early1980s the same one percentage point decline in real interest rates would cause asmaller 12 percent rise in the justifiable price-to-rent ratio Thus in the current lowreal interest rate environment a given decrease in real rates induces a largerpotential percentage increase in house prices than the same decrease in real rateswould cause starting from a high interest rate Of course the reverse is true too anunexpected rise in long-term real interest rates from their current low base wouldcause a disproportionately large percentage decline in the price people would bewilling to pay assuming rents stay constant

House Prices are More Sensitive to Changes in Real Interest Rates in HighAppreciation Rate Cities

We have thus far downplayed one of the most critical and least understooddeterminants of the user cost of housingmdashthe expected growth rate of housingprices Expected price appreciation is central to the debate over whether a housingbubble exists and if so where

Evidence suggests that expected rates of house price appreciation may differacross markets Because expected appreciation is subtracted from the user costmetropolitan areas where expected house price appreciation is high have loweruser costs than areas where expected house price appreciation is low Any givenchange in real interest rates then operates on a lower user cost base in high pricegrowth cities yielding a larger percentage effect For example if in the aboveexample we had assumed that the expected real rate of appreciation on housingwas 28 percent rather than 18 percent the predicted user cost would have been4 percent instead of 5 and the potential price-to-rent ratio would have been 25instead of 20 If we had assumed an even higher rate of expected price growth say38 percent the predicted user cost would have been 3 percent and the impliedprice-to-rent ratio would have been 333 (Lest this seem incredible that is aboutequal to the price-to-rent ratio in the San Francisco metropolitan area calculatedas the ratio of the mean house value divided by the mean rent in the 2000 Census)

12 Of course this calculation does not account for the fact that higher taxes would presumably causerents to fall the net effect of which may or may not cause housing prices to fall As Poterba (1990)explains whether this happens or not depends on the extent to which the increased tax differentialbetween and housing and nonhousing causes consumers to choose more housing


Thus a 1 percentage point decline in real interest rates could raise house prices byas much as 19 percent in a location that averaged 18 percent price growth locationand 33 percent in a 38 percent price growth market

A large degree of variation across cities in expected growth rates is plausiblegiven what we know about land price dynamics in cities Of course if the long-runsupply of housing were perfectly elastic house prices would be determined solely byconstruction costs and expected appreciation would be the expected growth ratein real construction costs (Muth 1960) At the national level real constructioncosts have fallen over the past 25 years yet over the same time period realconstant-quality house prices have grown When we looked at construction costs indifferent metropolitan areas using data from RS Means Company13 we found thathouse prices grew relative to construction costs in most of the metropolitanstatistical areas in our analysis Changes in construction costs explain neither theoverall rise in real house prices nor cross-sectional differences in appreciation ratesacross markets

The long-run growth of house prices in excess of construction costs suggeststhat the underlying land is appreciating faster than the structures This finding isconsistent with classic theories of urban development in which the growth of citiesis accompanied by (or driven by) benefits of increasing density and agglomerationSince the supply of land near urban centers (either a city center or suburbansubcenters) is in short supply the demand for housing generated by such economicgrowth is capitalized into land prices14 Indeed some metropolitan areas havepersistently high rates of house price appreciation over very long periods of timeGyourko Mayer and Sinai (2004) refer to cities with high long-run rates of houseprice growth as ldquosuperstar citiesrdquo They argue that because of tight supply con-straints combined with an increasing number of households who want to live in thearea a city can experience above-average house price growth over a very longhorizon They present evidence from the US Census since 1940 showing that realhouse price appreciation in superstar cities such as San Francisco Boston NewYork and Los Angeles has exceeded the national average by one to three percent-age points per year over a 60-year period In addition the average growth rate ofhousing prices over the 30-year period 1940ndash1970 has a correlation of 040 with thesubsequent 30-year average over 1970ndash2000 This fact suggests that differences inappreciation rates of housing across metropolitan statistical areas are persistentover long periods and reflect more than just secular changes in industrial concen-tration (like the high technology boom) or household preferences

A related finding is that price-to-rent differences across cities are persistentover time cities with high price-to-rent ratios tend to remain high and those withlow price-to-rent ratios remain low This fact is predicted by the user cost theory ifpurchasers in these ldquosuperstarrdquo markets are correctly anticipating sustained future

13 See Glaeser and Gyourko (2004) for more information on RS Means data and a detailed discussionof the issues involved in measuring construction costs across cities14 See the Spring 1998 ldquoSymposium on Urban Agglomerationrdquo in this journal for more detail on thesearguments especially the papers by Edward Glaeser (1998) and John Quigley (1998)


appreciation because buyers in superstar cities should be willing to pay more for ahouse if they expect rents to rise in the future Indeed Sinai and Souleles (2005)find statistical evidence using data from 44 metropolitan areas that the cities withthe highest price-to-rent ratios also have the highest expected growth rates of pricesand rents15

Calculating the User Cost

To examine how these cost factors are related to house price changes wecompute the user cost of housing for each of the 46 metro areas in our sample Asis often true with theoretical constructs solving for the user cost in practice posesa number of difficult challenges

First theory suggests that we measure the risk-free interest rate using arepresentative rate like the yield on a one-year US Treasury bill In practicehowever it is important to consider simultaneously the impact of expectedfuture real interest rates on the expected appreciation rate of future houseprices (the fifth term in our user cost equation) For example in 2004 realshort-term interest rates were well below real long-term rates suggesting thatthe bond market anticipated that real short-term interest rates would rise in thefuture When short-term rates are expected to rise potential homeownersshould also anticipate (assuming that land will be inelastically supplied in themarket) that the annual cost of ownership will also rise implying that futurehouse prices should fall (or rise less than they otherwise would) Thus a higherreal interest rate ldquospreadrdquomdashthe long-term minus short-term ratemdashsuggests arelatively lower expected growth rate of future house prices This predictabledecline in the future growth rate of house prices should roughly equal thespread Hence if we plug the spread into our user cost formula in place of gt1the short-term rate drops out and all that remains is the long-term rate In sumwhen a constant rate of future price appreciation is assumed for the user costformula it is probably more sensible to measure the opportunity cost of fundsusing a real long-term interest rate This subtle point is often ignored inempirical research In our calculations below we use the constant yield tomaturity on 10-year Treasuries and convert this to a real rate by subtracting the10-year expected inflation rate from the Livingston Survey If we were tosubstitute the one-year Treasury rate into our user cost calculations instead ofthe 10-year rate it would make housing in 2004 look even less costly in everymarket in our sample

15 We have found a similar persistence of price-to-rent ratios across markets using data from the 1980and 2000 Integrated Public Use Microsample from the US Decennial Census to compute the priceincome ratio at the household level for homeowners and the rentincome ratio at the household levelfor renters We then took the average of each ratio by metropolitan statistical area and decadePrice-to-rent at the metropolitan area-decade level is then calculated as MEAN(priceincome)MEAN(rentincome)


A second closely related challenge is that we cannot observe householdsrsquoexpected growth rate of housing prices By using long-term instead of short-term real interest rates in the user cost we already account for predictablechanges in house prices arising from predictable changes in short-term interestrates But house prices also rise in response to higher rents We assume that usercosts do not change over long horizons and that hence rents at the level of ametropolitan area grow at the same rate as real housing prices in that metroarea We therefore measure expected future rent growth using the average realgrowth rate of house prices from 1940 ndash2000 from Gyourko Mayer and Sinai(2004)

Finally it is important to recognize that metro areas also differ in their typicalfederal income taxes and local property tax rates Cities with higher per capitaincome have higher effective marginal income tax rates which lead to higherprice-to-rent ratios due to the greater value of the tax subsidy to owner-occupiedhousing We use average property tax rates from Emrath (2002) and income taxrates which we collect from the TAXSIM model of the National Bureau of Eco-nomic Research16 However data from the Internal Revenue Service show that65 percent of tax-filing households do not itemize their tax deductions and if theyare homeowners do not benefit from the tax deductibility of mortgage interest andproperty taxes17 To account at least roughly for the higher cost of owning for thenonitemizers we reduce the tax subsidy in our calculations by 50 percent

Table 2 shows how user cost can vary across cities and within cities over timeThe average user costs in the highest growth rate markets are less than half as bigas they are in the highest user cost marketsmdashfor example 33 percent in San Joseversus 71 percent in Pittsburgh This range of user costs implies average potentialprice-to-rent ratios ranging from 33 to 14 respectively By 2004 user costs hadfallen significantly below the long-run averagemdash20 percent in San Jose versus57 percent in Pittsburgh That is the price-to-rent ratio could have risen to 50 inSan Jose but just 18 in Pittsburgh To illustrate how much a low initial user costmatters for price swings San Francisco experienced a user cost decline from anaverage of 37 percent to 24 percent in 2004 implying a multiple expansion from27 to 42 In some cities user cost has declined more than 40 percent since theprevious house price peak while in other cities the decline has been as low as10 percent Since the user cost is just the inverse of the price-to-rent ratio these

16 While the net benefits of services financed by local property taxes presumably benefit owners andrenters equally a higher property tax nevertheless lowers the price-to-rent ratio because the tax isimplicitly included in rents but not home prices17 Sources IRS Statistics of Income Table 1mdashIndividual Income Tax All Returns Sources of Incomeand Adjustments and Table 3mdashIndividual Income Tax Returns with Itemized Deductions for 2002Available at httpwwwirsgovpubirs-soi02in01arxls and httpwwwirsgovpubirs-soi02in03gaxls Even without itemizing all homeowners benefit from some tax subsidy If a homeownerwere to rent his property out he would have to report the rent received as taxable income Ahomeowner does not need to report the ldquoimputed rentrdquo he pays himself as taxable income and thussaves the income tax he would have otherwise paid the government Gyourko and Sinai (2003) show thatthe mortgage interest and property tax deduction components comprise about one-third of the totalsubsidy


Table 2How User Cost Varies Across Cities and Over Time

Averageuser cost

User costin 2004

change inuser cost since

prior peak

change inuser cost sinceprior trough

Markets where house prices peaked in the late 1980s and had a trough in the 1990sAtlanta GA 53 39 31 26Austin TX 60 45 42 22Baltimore MD 57 43 29 22Boston MA 53 40 34 21Dallas TX 64 49 17 18District of Columbia DC 56 44 28 22Jacksonville FL 49 34 23 25Los Angeles CA 44 31 38 27Nashville TX 55 40 31 21New York NY 60 48 29 20Norfolk VA 56 42 33 23Oakland CA 42 29 39 30Orange County CA 37 25 41 33Philadelphia PA 66 52 27 19Phoenix AZ 47 33 23 6Portland OR 60 47 11 20Raleigh-Durham NC 52 38 19 20Richmond VA 61 47 27 21Sacramento CA 47 35 28 25San Bernadino-Riverside CA 46 33 36 26San Diego CA 41 29 40 30San Francisco CA 37 24 41 34San Jose CA 33 20 46 33Seattle WA 49 34 38 25

Markets where house prices were high in the early 1980s and rebounded in the 2000sCharlotte NC 52 38 35 6Chicago IL 63 49 30 24Cincinnati OH 65 51 14 22Cleveland OH 64 50 14 36Columbus OH 62 48 15 23Denver CO 51 37 42 28Detroit MI 66 52 11 41Fort Lauderdale FL 58 43 42 23Indianapolis IN 60 45 16 25Kansas City KS 57 42 16 7Memphis TN 60 45 29 23Miami FL 62 48 15 21Milwaukee MN 70 57 5 33Minneapolis MN 56 43 10 18Orlando FL 59 44 42 23Pittsburgh PA 71 57 12 18St Louis MO 61 47 15 7Tampa FL 61 46 18 22

Markets where house prices have declined since the early 1980s and never fully reboundedFort Worth TX 62 47 15 19Houston TX 67 52 38 20New Orleans LA 53 39 17 28San Antonio TX 69 54 42 23


examples demonstrate how heterogeneous changes in user cost can help explainthe heterogeneity of price and price-to-rent growth across cities More formally thecross-sectional correlation between the percentage change in user costs (the sec-ond column of Table 2) and the percentage change in price-to-rent (the thirdcolumn of Table 1) between 1995 and 2004 is 06

Finally it is worth noting that these computations do not account for all factorsthat could affect the spread in user costs between high and low appreciation ratecities In high-priced cities the value of structures is generally small relative to thevalue of the land on which they sit which suggests that physical depreciation issmall as a fraction of total property value Lower depreciation rates (relative tovalue) in high land cost markets such as San Francisco and New York would lowertheir user costs and further increase their price-to-rent ratios relative to other citiesOur calculations do not allow for this

At the same time lower depreciation rates in cities like San Francisco might beoffset by higher house price risk Some research has argued that housing inhigh-priced cities is riskier because the standard deviation of house prices is muchhigher (Case and Shiller 2004 Hwang and Quigley 2004) while other researchargues that homeowners can partially hedge this rent and price risk (Sinai andSouleles 2005)18 Since this hedge is imperfect however we would still expect riskpremiums would be somewhat higher in the highest price-to-rent markets narrow-ing the predicted cross-sectional difference in user costs between cities like SanFrancisco and Milwaukee say Our calculations do not allow for this effect either

Are Current House Prices Too HighOne way to assess whether house prices are too high is to calculate the imputed

rent on housing and compare it to actual rents available in the market Theimputed rent is the user cost times the current level of house prices obtained fromthe OFHEO price index

We create an index of the imputed-to-actual-rent ratio by dividing the imputedrent index by the index of market rents This index lets us assess whether theimputed cost of owning a house relative to renting the same unit has changedwithin each metropolitan area over time and whether the index in 2004 is at or nearits previous peak level We cannot make comparisons of imputed-to-actual-rentlevels across cities nor can we explicitly calculate whether the index is ldquotoo highrdquoat any given point in time but we can come close by comparing the index to its25-year average

We set the within-city 25-year average of the index equal to 10 in each cityFigure 2 plots the imputed-to-actual-rent index for 12 representative cities We alsoinclude the equivalent price-to-rent index for these cities to highlight the times thatthe price-to-rent index differs from the imputed-to-actual-rent ratio In Figure 2

18 Davidoff (2005) shows that the demand for owned housing decreases as the covariance between laborincome and house prices rises By that logic the price-to-rent ratio also should be lower in marketswhere the typical homebuyerrsquos wage income is closely tied to the local economymdashin one-company townsfor instance or cities with a high concentration in a single industry


Houston represents the small group of cities that have been experiencing decliningprices and imputed rent ratios over 25 years Chicago Detroit Miami and India-napolis are indicative of the middle panel cities that have U-shaped prices andimputed rent ratios The remaining cities are representative of the cyclical first

Figure 2Imputed-to-Actual-Rent Ratio versus Price-to-Rent Ratio(ratios normalized to their 25-year average)

51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco

Imputed rentActual rentPriceActual rent

Source Authorsrsquo calculations


Table 3User Cost-Based Assessments of House Price Levels

Imputed-to-actual-rent ratio Imputed-rent-to-income ratio

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Markets where the imputed rent-rent ratio peaked in the late 1980s and had a trough in the 1990sBoston MA 137 067 102 60 140 072 103 60District of Columbia DC 124 076 102 56 132 076 099 56Los Angeles CA 142 068 103 56 143 074 107 60New York NY 152 062 095 52 143 069 107 72Oakland CA 135 072 108 64 137 072 098 48Orange County CA 142 069 100 52 139 072 105 60Philadelphia PA 127 078 099 52 132 076 089 32Portland OR 127 075 113 76 126 077 100 48San Bernadino-Riverside CA 137 076 105 48 135 074 112 68San Diego CA 133 070 108 68 135 073 110 64San Francisco CA 152 071 097 48 145 072 092 44San Jose CA 151 067 104 64 146 070 084 20

Markets where the imputed rent-rent ratio was high in the early 1980s and flat or rising by the 2000sBaltimore MD 134 078 096 36 136 073 089 32Chicago IL 131 081 101 56 127 083 096 48Cincinnati OH 134 078 091 24 140 071 077 8Cleveland OH 125 082 096 36 129 078 084 8Columbus OH 122 081 093 28 143 071 077 8Denver CO 146 070 090 28 160 066 085 24Detroit MI 128 070 116 88 131 076 095 28Fort Lauderdale FL 145 073 107 60 161 071 104 64Jacksonville FL 153 073 088 32 168 067 080 28Kansas City KS 146 074 088 28 155 068 075 16Miami FL 131 077 112 80 152 074 112 72Milwaukee MN 115 079 114 88 128 083 093 32Minneapolis MN 123 079 114 88 145 073 097 56New Orleans LA 146 071 088 16 176 061 069 12Phoenix AZ 146 071 089 28 166 066 078 20Pittsburgh PA 122 084 097 32 141 073 078 8Sacramento CA 146 076 099 56 139 073 109 68Seattle WA 137 077 096 40 135 075 087 20St Louis MO 121 084 096 28 146 071 080 20Tampa FL 143 078 098 56 155 072 092 48

Markets where the imputed rent-rent ratio has declined since the early 1980s and never reboundedAtlanta GA 144 076 086 24 155 070 079 20Austin TX 155 072 079 16 171 066 070 8Charlotte NC 134 073 081 12 154 064 071 8Dallas TX 150 067 074 8 171 062 066 12Fort Worth TX 155 066 072 8 171 060 064 8Houston TX 176 068 074 12 181 062 066 16Indianapolis IN 122 081 090 20 148 066 070 4Memphis TN 137 070 076 8 156 060 064 4Nashville TX 132 075 084 12 155 063 069 8

Markets where the imputed rent-rent ratio has declined since the early 1980s and never rebounded (continued)Norfolk VA 149 073 088 20 149 069 086 24Orlando FL 150 075 088 36 158 072 084 36Raleigh-Durham NC 126 072 080 12 156 065 072 12Richmond VA 140 078 090 24 144 072 084 20San Antonio TX 161 067 074 12 183 059 062 8

Notes Twenty-five year within-MSA average is normalized to 100 Values are not comparable acrossmarkets

panel and include some of the highest profile markets on the east and west coastsTable 3 reports the value of the imputed-to-actual-rent index for all 46 cities atsome key dates

We highlight two important results First the imputed-to-actual-rent ratio doesnot suggest widespread or historically large mispricing of owner-occupied proper-ties in 2004 For all three groups of cities the imputed rent associated with buyinga house in 2004 is not nearly as high relative to actual rents as it was in the pastOnly seven cities have an imputed-to-actual-rent ratio that is within 20 percent of itsprevious peak and of those Detroit Milwaukee and Minneapolis are the closest Bycontrast 12 cities have imputed-to-actual-rent ratios 40 percent or more below theirhistorical peak levels In fact the 2004 levels of imputed-to-actual-rent are hardlyatypical In Portland Oregon the 2004 imputed-to-actual-rent ratio exceeds thevalue of the ratio in prior years about 75 percent of the time (88 percent inDetroit) But in high-price growth cities like Orange County and San Francisco theimputed-to-actual-rent ratio in the previous 24 years was higher than its 2004 valueabout half the time While owner-occupied housing is not nearly as expensiverelative to renting as it has been at times over the past 24 years housing in a fewmarkets appears somewhat expensive relative to the recent past

A second key observation is that deviations between the imputed rent andactual rent appear strongest when real interest rates were unusually high (early1980s) or unusually low (2001ndash2004) A comparison of Figures 2 and 3 makes thispoint quite clear Thus the recent run-up in house prices appears to be primarilydriven by fundamental economic changes Of course in Boston New York LosAngeles San Diego and San Francisco in the late 1980s and Denver Miami and

Figure 3Real 10-Year Interest Rates

Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10

Source Federal Reserve Bank H15 Release Federal Reserve Bank Livingston Survey 10-year expectedinflation


Houston in the mid-1980s a high imputed-to-actual-rent ratio was a prelude tosizable housing downturns Hence our methodology successfully identifies priorperiods of excessive valuations in the housing market

The imputed rent-to-income ratio provides an alternative measure of housingvaluations While the imputed-to-actual-rent ratio would be high if there were a housingbubble house prices could still fall if current housing costs were unsustainable givenhouseholdsrsquo abilities to pay The ratio of imputed rent to income provides a betterindicator of whether house prices are supported by underlying demand In particularrising housing prices or rising user costs need not imply that households are beingpriced out of the market if incomes are rising too In a bubble market by contrast wewould expect to see the annual cost of homeownership rising faster than incomes thusraising imputed rent-to-income to unsustainable levels

Figure 4 reports the ratio of the imputed rent of owner-occupied housing toincome for 12 cities with a matching panel in Table 319 For comparison Figure 4also shows the more commonly used metric of house price-to-income These ratioswere calculated in the same way as the imputed-to-actual-rent ratio except thatwe now divide imputed rent by an index of income per capita at the level of themetropolitan statistical area constructed from BEA data We set the index valueof 10 to correspond to the 25-year average for each metropolitan area of eachratio

These calculations lead us to similar conclusions as in the previous sectionNone of the metropolitan areas that we have featured appears to be at a peak levelof costliness in 2004 (relative to the past 24 years) In fact only nine of our 46 citieshave housing costs above their average historical levels relative to per capitaincome Even so the range across cities in the 2004 imputed rent-to-incomemeasure which varies from 112 in Miami to 062 in San Antonio is much tighterthan the within-city variation over time Indeed in Miami the imputed rent-to-income ratio has a historical peak-trough range of more than 50 percent Onemight object to these historical comparisons on the grounds that prices during the1980s were unusually high If we remove the 1980s data it is still the case that onlysouthern California and south Florida look relatively expensive

Outliers for the ratios of imputed-rent-to-income or price-to-income are mostpronounced in years when real interest rates are historically high or historically lowJust as with our imputed-to-actual-rent ratio a high imputed-rent-to-income ratiohas been a prelude to subsequent price declines Despite having been corrected torecognize differences in user costs the imputed rent-to-income ratio comes with animportant caveat Households at the median income may be poorer than themarginal buyers of median-priced homes For example Gyourko Mayer and Sinai(2004) argue that the marginal homebuyers in ldquosuperstarrdquo cities are high-incomehouseholds who have moved from other parts of the country This pattern wouldimply that the median homes in such cities are purchased by new residents whose

19 In an appendix appended to the on-line version of this paper at httpwwwe-jeporg Figure 2 shows ourcalculated imputed-to-actual-rent ratios for another 33 metropolitan statistical areas in our data Also in theappendix Figure 3 shows our calculated imputed-to-actual-rent ratios for another 33 metropolitan areas


income exceeds that of the median income Also Ortalo-Magnegrave and Rady (2005)argue that homeowners whose incomes do not grow as fast as house prices in anarea can remain homeowners since their housing wealth rises commensurately withprices Thus they can appear to be low income but that is because the implicit

Figure 4Imputed-Rent-to-Income Ratio versus Price-to-Income Ratio(ratios normalized to their 24-year average)

5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York

Imputed rentIncomePriceIncome



income from their housing wealth is not reflected in the denominator of theimputed rent-to-income ratio For these reasons our measure of the imputedrent-to-income ratio in high price growth markets is likely to be higher than thetrue underlying concept Ideally we would also want to consider wealth ratios whenassessing the extent of housing market excess

But even given this caveat housing prices at the end of 2004 did not lookparticularly out of line with past patterns of rents or incomes This conclusion holdstrue even in cities like Boston New York and San Francisco where housing priceswere high and rose even higher from 2000 to 2004 Only a handful of citiesmdashnamely Miami Fort Lauderdale Portland and San Diegomdashhad imputed-to-actual-rent and imputed rent-to-income ratios that both were higher relative to the recentpast but even they had not yet risen to approach previous historical peak levels

What Might Be Missing in these CalculationsOne obviously important factor over the past 25 years that we have omitted

from our analysis is cost-reducing innovations in the mortgage market Data fromthe Federal Housing Finance Board suggest that in 1980 initial fees and ldquopointsrdquo (apoint is an up-front fee equal to 1 percent of the loan amount) were typically about2 percent of the value of a loan by 2004 they were less than one-half of 1 percentLower origination costs may be a factor in the greater willingness of homeownersto refinance their mortgage in response to decreases in interest rates (BennettPeach and Peristiani 2001) Since lower origination costs are likely a permanentchange in the mortgage market demand for housing may be permanently higherlowering the imputed rent associated with owning a house in the latter portion ofour sample period

An alternative hypothesis sometimes advanced for the recent rapid growth inhousing prices is that it has become easier to borrow so that overall demand forhomeowning has risen Certainly average mortgage amounts have risen muchfaster than inflation or incomes growing by over $120000 since 1995 to a recordhigh of over $261000 in 2003 Yet average down payments amounts have growneven faster The average new first mortgage in 2003 had a down payment thatexceeded 25 percent of the house value nearly five percentage points higher thanin 1995 Lest one fear that these aggregate statistics mask a number of liquidity-constrained households the percentage of household with a loan-to-value ratio ator above 90 percent fell from 25 percent to 20 percent of all borrowers between1997 and 2003 In addition down payment percentages are the highest (and thusloan-to-value ratios are lowest) in the most expensive cities Down payments forbuyers in San Francisco and New York averaged 39 and 34 percent of their housevalue respectively We suspect that this occurs in part because existing homeownersuse large capital gains to put more money down on their next house One caveatif homeowners are extracting equity using second mortgages and doing so dispro-portionately in the most expensive cities our data would underestimate their trueleverage However it would take considerable borrowing through that channel tooffset the increase in down payments we observe

A related concern is that if many people have borrowed using adjustable rate


mortgages they may be especially vulnerable to an in increase in interest ratesFrom 2001ndash2003 adjustable rate mortgages made up fewer than 20 percent of allnew mortgages a rate that is lower than all but one year in the 1990s However theuse of adjustable rate mortgages did rise to 34 percent of new mortgages in 2004While buyers in high-priced cities are more likely than average to take out adjust-able rate mortgages the use of adjustable rate mortgages also fell disproportion-ately in the most expensive cities with declines from 1995ndash2003 of 21 and 16 per-cent in San Francisco and New York respectively (the decline in Boston was smaller)

Even so if any of the above considerations were actually spurring spending inthe housing market our analysis should pick it up as more costly homeownershipDemand driven by looser restrictions on obtaining credit for example should leadto higher house prices In our analysis the higher price would raise the imputedrent and the imputed-to-actual-rent and imputed rent-to-income ratios would riseWe do not observe this at least by the end of 2004 The last time such factors(anecdotally at least) affected the housing market was in the late 1980s and thatmispricing is very apparent in our data

Yet another potential shortcoming of our analysis is that we assume low-costarbitrage between owning and renting In reality mortgage origination fees brokercommissions and moving costs make it expensive to switch back and forth betweenowning and renting These transaction costs imply a range within which imputedrents may deviate from actual rents before market forces work to close the gapArbitrage arguments may also fail because the characteristics of rental units andowned homes may differ substantially in which case imputed rent comparisons areless meaningful

Finally our valuation ratios may be biased due to trends in unmeasured qualitydifferences between owned and rented units We have no reason to believe that thequality of owner-occupied housing has systematically declined relative to that ofrental housing If anything the reverse is likely true due to the strong growth inhome renovation expenditures If the quality of existing single-family homes isrising the OFHEO price index might overstate housing appreciation rates for aconstant quality house

Conclusion

We have discussed how to calculate the local annual cost of owner-occupiedhousing and how to construct measures of home values by comparing this cost tolocal incomes and rents Doing so reveals past periods like the late 1980s when thecost of owning looked quite high relative to incomes or the cost of renting In 2004however these same measures show little evidence of housing bubbles in almostany of the markets we have studied Constant-quality data on house prices and rentsexist for less than three decades cover only two house price booms and are notcomparable across different cities Hence it is impossible to state definitivelywhether or not a housing bubble exists However we can say that most housingmarkets did not look much more expensive in 2004 than they looked over the past


10 years and in most major cities our valuation measures are nowhere near theirhistoric highs

We hope that three main insights emerge from our analysis First house pricedynamics are a local phenomenon and national-level data obscure importanteconomic differences among cities Moreover one cannot draw conclusions abouthouse prices by comparing cities price-to-income and price-to-rent ratios thatwould be considered ldquohighrdquo for one city may be typical for another Second whenconsidering local house prices the economically relevant basis for comparison isthe annual cost of ownership Without accounting for changes in real long-terminterest rates expected inflation expected house price appreciation and taxes onecannot accurately assess whether houses are reasonably priced Third changes inunderlying fundamentals can affect cities differently In particular in cities wherehousing supply is relatively inelastic prices will be higher relative to rents andhouse prices will typically be more sensitive to changes in interest rates

Our evidence does not suggest that house prices cannot fall in the future iffundamental factors change An unexpected rise in real interest rates that raiseshousing costs or a negative shock to a local economy would lower housingdemand slowing the growth of house prices and possibly even leading to a houseprice decline However this fact does not mean that today houses are systematicallymispriced

y The authors would like especially to thank Steven Kleiman and Reed Walker for extraor-dinary research support and Joseph Gyourko James Hines Steven Kleiman JonathanMcCarthy Richard Peach Allen Sinai Timothy Taylor Michael Waldman Neng Wang andseminar participants at Columbia Business School for many helpful comments The PaulMilstein Center for Real Estate at Columbia Business School and the ZellLurie Real EstateCenter at Wharton provided funding The views expressed in the paper are solely those of theauthors and do not necessarily reflect the position of the Federal Reserve Bank of New York orthe Federal Reserve System

References

Ayuso Juan and Fernando Restoy 2003ldquoHouse Prices and Rents An Equilibrium Ap-proachrdquo Working Paper No 0304 Banco deEspana

Bennett Paul Richard Peach and StavrosPeristiani 2001 ldquoStructural Change in the Mort-gage Market and the Propensity to RefinancerdquoJournal of Money Credit and Banking 334pp 955ndash75

Brueckner Jan K 1987 ldquoThe Structure of

Urban Equilibria A Unified Treatment of theMuth-Mills Modelrdquo in Handbook of Regional andUrban Economics Edwin S Mills ed AmsterdamNorth Holland pp 821ndash45

Case Karl E and Robert J Shiller 1988 ldquoTheBehavior of Home Buyers in Boom and Post-Boom Marketsrdquo New England Economic ReviewNovemberDecember 80 pp 29ndash46

Case Karl E and Robert J Shiller 1989 ldquoTheEfficiency of the Market for Single Family


Homesrdquo American Economic Review March 791pp 125ndash37

Case Karl E and Robert J Shiller 2004 ldquoIsThere a Bubble in the Housing Marketrdquo Brook-ings Papers on Economic Activity 2 pp 299ndash342

Cocco Joao 2000 ldquoHedging House PriceRisk with Incomplete Marketsrdquo Working paperLondon Business School

Cocco Joao F and John F Campbell 2003ldquoHousehold Risk Management and OptimalMortgage Choicerdquo Quarterly Journal of EconomicsNovember 1184 pp 1449ndash494

Davidoff Thomas 2003 ldquoLabor IncomeHousing Prices and Homeownershiprdquo MimeoUC Berkeley

Emrath Paul 2002 ldquoProperty Taxes in the2000 Censusrdquo Housing Economics December5012 pp 16ndash21

Engelhardt Gary 1994 ldquoHouse Prices and theDecision to Save for Down Paymentsrdquo Journal ofUrban Economics September 362 pp 209ndash37

Engelhardt Gary 1996 ldquoConsumption DownPayments and Liquidity Constraintsrdquo Journal ofMoney Credit and Banking May 282 pp 255ndash71

Flavin Marjorie and Takashi Yamashita 2002ldquoOwner-Occupied Housing and the Composi-tion of the Household Portfoliordquo American Eco-nomic Review March 921 pp 345ndash62

Genesove David and Christopher Mayer1997 ldquoEquity and Time to Sale in the Real EstateMarketrdquo American Economic Review 873pp 255ndash69

Genesove David and Christopher Mayer2001 ldquoLoss Aversion and Seller Behavior Evi-dence from the Housing Marketrdquo Quarterly Jour-nal of Economics 1164 pp 1233ndash260

Glaeser Edward L 1998 ldquoAre Cities DyingrdquoJournal of Economic Perspectives 122 pp 139ndash60

Glaeser Edward L and Joseph Gyourko2005 ldquoDurable Housing and Urban DeclinerdquoJournal of Political Economy 1132 pp 345ndash75

Glaeser Edward L and Albert Saiz 2003ldquoThe Rise of the Skilled Cityrdquo NBER WorkingPaper No 10191

Glaeser Edward L Joseph Gyourko andRaven E Saks 2004 ldquoWhy Have Housing PricesGone Uprdquo Working paper

Gyourko Joseph and Todd Sinai 2003 ldquoTheSpatial Distribution of Housing-Related Ordi-nary Income Tax Benefitsrdquo Real Estate EconomicsWinter 314 pp 527ndash76

Gyourko Joseph Christopher Mayer andTodd Sinai 2004 ldquoSuperstar Citiesrdquo Workingpaper Columbia Business School and WhartonSchool

Hanushek Eric A and John M Quigley 1980ldquoWhat is the Price Elasticity of Housing De-

mandrdquo Review of Economics and Statistics 623pp 449ndash54

Harding John P Stuart S Rosenthal andC F Sirmans 2004 ldquoDepreciation of HousingCapital and the Gains from HomeownershiprdquoWorking paper Syracuse University Available athttpfacultymaxwellsyredurosenthal

Hendershott Patric and Joel Slemrod 1983ldquoTaxes and the User Cost of Capital for Owner-Occupied Housingrdquo Journal of the American RealEstate and Urban Economics Association Winter104 pp 375ndash93

Henderson J V 1977 Economic Theory and theCities New York Academic Press

Hurst Erik and Frank Stafford 2004 ldquoHomeis Where the Equity Is Liquidity ConstraintsRefinancing and Consumptionrdquo Journal of MoneyCredit and Banking December 36 pp 985ndash1064

Hwang Min and John M Quigley 2004 ldquoEco-nomic Fundamentals in Local Housing MarketsEvidence from US Metropolitan RegionsrdquoMimeo UC Berkeley February

Lamont Owen and Jeremy Stein 1999 ldquoLe-verage and House Price Dynamics in US Cit-iesrdquo Rand Journal of Economics Autumn 30pp 466ndash86

Mayer Christopher 1993 ldquoTaxes IncomeDistribution and the Real Estate Cycle Why AllHouses Donrsquot Appreciate at the Same Raterdquo NewEngland Economic Review MayJune pp 39ndash50

Mayo Stephen 1981 ldquoTheory and Estimationin the Economics of Housing Demandrdquo Journalof Urban Economics 101 pp 95ndash116

Meese Richard and Nancy Wallace 1993ldquoTesting the Present Value Relation for HousingPrices Should I Leave My House in San Fran-ciscordquo Journal of Urban Economics 353 pp 245ndash66

McCarthy Jonathan and Richard W Peach2004 ldquoAre Home Prices the Next Bubblerdquo Fed-eral Reserve Bank of New York Economic Policy Re-view 103 pp 1ndash17

Merlo Antonio and Francois Ortalo-Magne2004 ldquoBargaining over Residential PropertiesEvidence from Englandrdquo Journal of Urban Eco-nomics September 56 pp 192ndash216

Moretti Enrico 2004 ldquoWorkersrsquo EducationSpillovers and Productivity Evidence from Plant-Level Production Functionsrdquo American EconomicReview 943 pp 656ndash90

Muth R F 1960 ldquoThe Demand for NonfarmHousingrdquo in The Demand for Durable Goods A CHarberger ed Chicago University of ChicagoPress chapter 2

Muth R F 1969 Cities and Housing ChicagoUniversity of Chicago Press

Ortalo-Magnegrave Francois and Sven Rady 1999ldquoBoom In Bust Out Young Households and the


Housing Cyclerdquo European Economic Review April43 pp 755ndash66

Ortalo-Magnegrave Francois and Sven Rady 2001ldquoHousing Market Dynamics On the Contribu-tion of Income Shocks and Credit ConstraintsrdquoDiscussion Paper No 470 CESIfo

Piazzesi Monika Martin Schneider and SelaleTuzel 2003 ldquoHousing Consumption and AssetPricingrdquo Working paper

Poterba James 1984 ldquoTax Subsidies toOwner-occupied Housing An Asset Market Ap-proachrdquo Quarterly Journal of Economics 994pp 729ndash52

Poterba James 1991 ldquoHouse Price DynamicsThe Role of Tax Policy and Demographyrdquo Brook-ings Papers on Economic Activity 2 pp 143ndash83

Quigley John M 1998 ldquoUrban Diversity andEconomic Growthrdquo Journal of Economic Perspec-tives 122 pp 127ndash38

Rosenthal Stuart S and William C Strange2004 ldquoEvidence on the Nature and Sources ofAgglomeration Economiesrdquo in Handbook of Ur-ban and Regional Economics Volume 4 J V Hen-

derson and J-F Thisse eds AmsterdamElsevier pp 2119ndash172

Shiller Robert J 1993 Macro Markets CreatingInstitutions for Managing Societyrsquos Largest EconomicRisks Oxford Oxford University Press

Sinai Todd and Nicholas Souleles 2005ldquoOwner Occupied Housing as a Hedge AgainstRent Riskrdquo Quarterly Journal of Economics May1202 pp 763ndash89

Stiglitz Joseph E 1990 ldquoSymposium on Bub-blesrdquo Journal of Economic Perspectives Spring 42pp 13ndash18

Stein Jeremy 1995 ldquoPrices and Trading Vol-ume in the Housing Market A Model withDownpayment Effectsrdquo Quarterly Journal of Eco-nomics May 110 pp 379ndash406

Terrones Marcos 2004 ldquoThe Global HousePrice Boomrdquo in The World Economic Outlook TheGlobal Demographic Transition Washington DCIMF pp 71ndash89

Venti Steven and David Wise 1991 ldquoAgingand the Income Value of Housing Wealthrdquo Jour-nal of Public Economics 443 pp 371ndash97


pricemdashthen a bubble exists At least in the short run the high price of the asset ismerited because it yields a return (capital gain plus dividend) equal to that onalternative assetsrdquo The ldquodividendrdquo portion of the return from owning a housecomes from the rent the owner saves by living in the house rent-free and the capitalgain comes from house price appreciation over time We think of a housing bubbleas being driven by homebuyers who are willing to pay inflated prices for housestoday because they expect unrealistically high housing appreciation in the future1

In this paper we explain how to assess the state of house pricesmdashboth whetherthere is a bubble and what underlying factors support housing demandmdashin a waythat is grounded in economic theory In doing so we correct four common fallaciesabout the costliness of the housing market First the price of a house is not thesame as the annual cost of owning so it does not necessarily follow from risingprices of houses that ownership is becoming more expensive Second high pricegrowth is not evidence per se that housing is overvalued In some local housingmarkets house price growth can exceed the national average rate of appreciationfor very long periods of time Third differences in expected appreciation rates andtaxes can lead to considerable variability in the price-to-rent ratio across marketsFinally the sensitivity of house prices to changes in fundamentals is higher at timeswhen real long-term interest rates are already low and in cities where expectedprice growth is high so accelerating house price growth and outsized price in-creases in certain markets are not intrinsically signs of a bubble For all of the abovereasons conventional metrics for assessing pricing in the housing market such asprice-to-rent ratios or price-to-income ratios generally fail to reflect accurately thestate of housing costs To the eyes of analysts employing such measures housingmarkets can appear ldquoexuberantrdquo even when houses are in fact reasonably pricedWe construct a measure for evaluating the cost of home owning that is standard foreconomistsmdashthe imputed annual rental cost of owning a home a variant of theuser cost of housingmdashand apply it to 25 years of history across a wide variety ofhousing markets This calculation enables us to estimate the time pattern ofhousing costs within a market Given the relatively short time series it is hard to sayjust how costly a market must become before it is unsustainably expensive However wecan determine how expensive a market is currently relative to its own history

As of the end of 2004 our analysis reveals little evidence of a housing bubbleIn high-appreciation markets like San Francisco Boston and New York currenthousing prices are not cheap but our calculations do not reveal large priceincreases in excess of fundamentals For such cities expectations of outsized capitalgains appear to play at best a very small role in single-family house prices Ratherrecent price growth is supported by basic economic factors such as low reallong-term interest rates high income growth and housing price levels that hadfallen to unusually low levels during the mid-1990s The growth in price-to-rentratiosmdashespecially in cities where this ratio was already highmdashcan be explained bythe fact that house prices are more sensitive to real long-term interest rates when

1 Case and Shiller (2004) also use this definition












PriceIncome

Real price

PriceRent

1980

8

1

12

14

1985 1990 1995 2000 2005





Atpriorpeak

At priortrough

In2004

Atpriorpeak

At priortrough

In2004

Atpriorpeak

At priortrough

In2004


































Rt Ptut


ut rtrf t trt

m t t gt1 t



Rit Pit 1 it






































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References




































































PriceIncome

Real price

PriceRent

1980

8

1

12

14

1985 1990 1995 2000 2005





Atpriorpeak

At priortrough

In2004

Atpriorpeak

At priortrough

In2004

Atpriorpeak

At priortrough

In2004


































Rt Ptut


ut rtrf t trt

m t t gt1 t



Rit Pit 1 it






































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References






























































PriceIncome

Real price

PriceRent

1980

8

1

12

14

1985 1990 1995 2000 2005





Atpriorpeak

At priortrough

In2004

Atpriorpeak

At priortrough

In2004

Atpriorpeak

At priortrough

In2004


































Rt Ptut


ut rtrf t trt

m t t gt1 t



Rit Pit 1 it






































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References




























































Atpriorpeak

At priortrough

In2004

Atpriorpeak

At priortrough

In2004

Atpriorpeak

At priortrough

In2004


































Rt Ptut


ut rtrf t trt

m t t gt1 t



Rit Pit 1 it






































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References























































































Rt Ptut


ut rtrf t trt

m t t gt1 t



Rit Pit 1 it






































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References
















































































Rt Ptut


ut rtrf t trt

m t t gt1 t



Rit Pit 1 it






































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References







































































Rt Ptut


ut rtrf t trt

m t t gt1 t



Rit Pit 1 it






































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References































































Rt Ptut


ut rtrf t trt

m t t gt1 t



Rit Pit 1 it






































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References




























































































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References



















































































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References












































































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References






































































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References
































































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References



























































Averageuser cost

User costin 2004


prior peak

















51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References





































































51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References




























































51

15

2

1980 1990 2000

Boston

51

15

2

1980 1990 2000

Chicago

51

15

2

1980 1990 2000

Denver

51

15

2

1980 1990 2000

Detroit

51

15

2

1980 1990 2000

Houston

51

15

2

1980 1990 2000

Los Angeles

51

15

2

1980 1990 2000

Miami

51

15

2

1980 1990 2000

Minneapolis

51

15

2

1980 1990 2000

New York

51

15

2

1980 1990 2000

Portland

51

15

2

1980 1990 2000

San Diego

51

15

2

1980 1990 2000

San Francisco






Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References




























































Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004

Atsamplepeak

Atsampletrough

In2004

ofyearsbelow2004










Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References






























































Inte

rest

rat

e

1980 1985 1990 1995 2000 2005

0

25

5

75

10












5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References



































































5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References




























































5 1

512

1980 1990 2000

Boston

51

512

1980 1990 2000

Chicago

51

512

1980 1990 2000

Denver

51

512

1980 1990 2000

Detroit

51

512

1980 1990 2000

Houston

51

512

1980 1990 2000

Los Angeles

51

512

1980 1990 2000

Portland

51

512

1980 1990 2000

San Diego

51

512

1980 1990 2000

San Francisco

51

512

1980 1990 2000

Miami

51

512

1980 1990 2000

Minneapolis

51

512

1980 1990 2000

New York















Conclusion







References





































































Conclusion







References






























































Conclusion







References






























































References



























































































































Documents

Assessing High House Prices: Bubbles, Fundamentals and ... · markets can appear ÒexuberantÓ even when houses are in fact reasonably priced. We construct a measure for evaluating