The Real Estate Market Index

The Real Estate Market Index

David Zetland*

The Real Estate Market Index (“REMI”) combines sales price, sales volume and days on

market into a summary measure of market activity or liquidity. The REMI, which rises with

price or volume and falls with days on market, is more sensitive to market sentiment than

indices based on price alone, e.g., the Case-Schiller Index. The REMI is useful to people

who want a measure of market liquidity. Data from over 19,000 sales that occurred be-

tween January 2000 and November 2009 in Mission Viejo, California illustrate the calcula-

tion, calibration and application of the REMI.

Economists describe markets as a placewhere supply and demand meet, the upwardsloping supply curve intersecting the down-ward sloping demand curve at an equilibriumprice where the quantity supplied equalsquantity demanded. Although markets arerarely in equilibrium—supply and demand areconstantly changing—this concept, this use-ful �ction, is used to explain how marketforces are interacting, pushing price andquantity up and down. In some markets (e.g.,spot markets for gold, blue chip stocks, trea-sury bonds, and so on) this stylized view ofthe market is fairly accurate—daily closingprices provide a fairly accurate representa-tion of “where the market is.” In other less-liquid markets, prices do not give a very goodpicture of market activity. The �ne art “mar-ket,” for example, consists of many pieces—often unique—selling at auction, by negotia-tion, among dealers, and other channels.Prices in the art market do not thereforecapture the full complexity of the dynamicsof supply and demand. The job market forrecent graduates with doctoral degrees is

similar: the “prices” (salary o�ers) thatemerge characterize neither equilibrium northe process of matching supply and demandvery well.

The market for residential real estate issomewhere between these extremes. Thesales price of a house does not fully describehow supply and demand interacted in thesale of that house. Home sales, on the otherhand, share certain characteristics that allowone to aggregate them, to get an idea ofactivity in the market for homes. Talk to anyrealtor, and you will hear them describe the“market’ in a way that re�ects these nuances.They will tell you about closing prices butalso mention days of inventory (unsold homesdivided by the number of sales per day), dayson market (how long before a house that'slisted for sale gets an acceptable o�er),seasonality (more houses sell in summer,during school breaks), and so on. Many ofthese indicators are useful, but they are hardto compare and reconcile. Talk to one realtorand you get one view of the market; talk to

*David Zetland, Wantrup Fellow, Agricultural and Resource Economics, UC Berkeley, can be reached [email protected]. The author thanks Hugh Zetland for advice and MLS data, Mike Shedlock for useful insights,and several reviewers for their helpful comments.

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another and that same market can look quitedi�erent. Although it is probably possible toreconcile these di�erent views, it is also timeconsuming and confusing, which means thatoutsiders—pretty much everyone who is notworking full time in the area—have a hardtime knowing just how the market is doing.Where is liquidity this month, as opposed tolast month, or last year? Is the market hot ornot? Investors are especially interested inthis question, as they want to compare manymarkets in many places, to understand rela-tive performance.

An index provides a summary measure, themarket in one number. Percentage changesin indices make it easy to compare di�erentmarkets over time, even when they are basedon di�erent assets or units. Thus one mightcompare Hong Kong's Hang Seng to the DowJones Industrial Average, precious metals tobulk raw sugar. These comparisons assumeimplicitly that the price component of theindex includes all important information. Ifsomething other than price matters, turnoveras a measure of liquidity for example, thenthe comparison may indeed be more apples-to-oranges than apples-to-apples.

Many people use the Case-Schiller Index(“CSI”) to describe the real estate market,1

but the CSI is better at describing homevalues than market activity, or liquidity. Aswe know, liquidity is not just about price, butalso sales volume and transaction speed, anda description of the real estate market shouldincorporate that information. What we needis an index that captures more information,

since real estate market performance is notjust a function of price.

This article presents an index that includesthis information, a Real Estate Market Index(“REMI”) that combines median sales price,volume (number of sales) and median dayson market (“DoM”) into a single measure ofoverall market activity. The REMI allowscomparisons between periods (e.g., January2004 and January 2008) and/or areas (e.g.,San Francisco and Los Angeles).2 As anexample, I calculate the REMI for MissionViejo (MV, a city of about 100,000 people inSouthern California's Orange County) usingdata from over 19,000 sales that closedescrow between January 2000 and Novem-ber 2009.

Next, this article reviews the literature onprice indices, liquidity and activity in realestate markets. It then de�nes the compo-nents used to construct the REMI and theirrelative weights in the index. Before conclud-ing, this article discusses the REMI's ac-curacy and how it might be used.

Real Estate Markets

This section describes and compares themost widespread measure of market activ-ity—the Case-Schiller Index of repeat salesprices—to broader measures of the market,such as market liquidity (being able to sell ahouse at full price) and market activity (vol-ume and velocity). Throughout this section, Itest predictions from the literature with MVdata, which are shown in Figure 1 and listed,with REMI values, in Table 1.



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Price Indices

Case and Shiller (1987) described a quar-terly index of weighted, repeat sales thatshows smaller changes in home values thanan index of median prices for all sales, whichCase and Schiller argued are biased upwardsby the inclusion of new home sales. Theywere rewarded for this observation: The CSIis the most widely cited measure of prices inthe housing market.3 Today, a three monthrolling average CSI for 20 metropolitanstatistical areas is published with a twomonth lag, e.g., the CSI published on the lastTuesday in February re�ects average pricesin October/November/December (Standard& Poor's, 2008). This rolling average designmeans that the CSI changes rather slowly,e.g., the correlation between the CSI for LosAngeles and its value from three months

earlier is 0.994.4 Case and Shiller (1989) citethis result as evidence that “the market forsingle family homes does not appear to bee�cient.” But is e�ciency in the real estatemarket only a function of price? Case andSchiller would probably agree that it is not,and so would most people who participate inreal estate markets. A broader de�nition ofe�ciency in the real estate market wouldinclude “liquidity,” a many-splendored word.

Liquidity and Market Activity

Kluger and Miller (1990) declare that liquid-ity is the probability of selling a house—rela-tive to the probability of selling anotherhouse—at market value. They are careful tosay that their measure is “not quite the same”as its inverse—DoM—mostly because theyare measuring the ex-ante probability of a



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sale whereas DoM is an ex-post result thatdepends upon unpredictable, heterogeneousshocks connected with a particular sale.5

The model of the housing market in Stein(1995) integrates prices, sales and DoM. Hesuggests that more liquidity results in hotmarkets (higher sales and lower DoM) be-cause a seller with greater equity will be anaggressive buyer. The “most robust predic-tion” of Stein's model is that prices and salesare positively correlated [p. 398]. MV data donot strongly reject his hypothesis for contem-porary data (i.e., the correlation between thechange in price and sales in the same periodis 0.10), but there is zero correlation betweenprice in one month and sales one, two orthree months later. Another prediction—thatthere will be more “�shing” (listing but thencanceling) when price and sales are low—isonly partially con�rmed in the data. A simpleOLS regression of normalized measures ofcanceled deals on price and sales shows sig-ni�cant positive correlations with price andnegative correlations with sales, i.e., thehypothesized relationship is rejected with re-spect to price but not for sales.6

Berkovic and Goodman (1996) create amacroeconomic measure of housing demand,which they compare to price and salesturnover. They conclude that, “for highfrequency data, turnover is a superior to priceas an indicator of change in housing demand”[p. 421]. indicator of change in housingdemand.” The mechanism for this superior-ity? Changes in demand a�ect turnover morequickly than they a�ect price. These observa-tions are supported in the data: Normalizedprice and sales (1.00 in Jan 2000) have sim-ilar means (1.88 and 1.81) and standarddeviations (0.52 and 0.54) over the entiresample period, but their standard deviationsfor month-on-month changes are 0.10 and

0.40, respectively. Sales are more volatile;see Figure 2.

Krainer (2001) says a hot market has ris-ing prices, above average volume and shortselling times (DoM). He equates fast, full pricesales to liquidity and calculates—using aparameterized and stylized model—that“liquidity is much more variable than prices”—an assertion that is not rejected in MV data[p. 49]. The standard deviation of monthlychanges in normalized prices (as above) is0.10, but the standard deviation of monthlychanges in DoM is 0.45.

Clayton et al. (2008) say that liquidity is ajoint function of price and sales volume.7

Echoing Stein (1995), they also say that“turnover appears to lead price movements”[p. 20]. The 0.05 correlation between thechange in sales and change in price in thenext month does not reject their claim, but italso fails to provide strong evidence in favor.

Novy-Marx (2009) elaborates on the liquid-ity story, explaining that cycles are magni�edby feedback loops, i.e., a sudden increase inthe number of buyers lowers DoM and re-moves sellers too quickly from the market—creating a shortage of houses and pricebubble.8 Unfortunately, bivariate relations inMV data reject this relationship: Median priceis 87 percent of the average (mean of monthlymedian prices) when median DoM are belowaverage and 119 percent of average inmonths where DoM are above average.9 Healso predicts that “tight” markets will havehigher prices and lower DoM in roughly simi-lar proportions, i.e., a one percent increase intightness results from one half percentincrease in price and one half percent de-crease in DoM. These predications do notshow up in sample data: Normalized prices inthe MV data have half the standard deviation



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of normalized DoM, which means that eitherchanges in price should get twice the weightof DoM or DoM explain two thirds of tight-ness in the market. (See below for morediscussion on weights.)

Calculating the REMI

The REMI is an index of past market condi-tions that relies on data from completedsales. This section describes the threevariables that compose the REMI, explains

how the REMI is calculated, and calculatesthe REMI for Mission Viejo.

REMI Components

The REMI combines median sales price,number of sales, and median DoM from allescrows closed in a given month in a givenarea. Figure 2 gives normalized values (to1.00 in Jan 2000) for Mission Viejo datashown in Figure 1.

Consider the intuition of how each variabledescribes a hot market, i.e., prices are rising,sales volume is rising, and DoM are falling.Sales rise during the high season (generallysummer) when there are more buyers andsellers. In the o�-season or a slowing mar-ket, sales fall because fewer buyers and sell-ers are in the market, and buyers take longerto search through inventory.10 Note that a

summer market has higher sales but not nec-essarily higher prices. DoM fall in a hot mar-ket, e.g., DoM = 0 indicates that a house sells

the day it is listed.11 Although all of theseindicators may change in a hot market, theymay not change at the same rate. Thisimperfect correlation is common, and thosewho study only price or sales or DoM may



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overlook liquidity changes re�ected in othercomponents.

All three variables are normalized to 1.00for two reasons. First, normalization removesunits, so that prices can be compered toDoM, relative to a baseline point in time, as apercentage change. If prices go to 1.10 andDoM rise to 1.10, then we know that theyhave both changed by ten percent. Second,normalization allows di�erent areas to becompared, sales volume in New York may gofrom 1.00 to 1.05 in the same period as itgoes from 1.00 to 1.10 in Topeka. Such acomparison allows one to see that the changein Topeka home sales volume is greater thanthe change in New York volume. Normaliza-tion does not indicate if a 10 percent changein price is more important than a 10 percentchange in DoM, or if the New York market isworth more than the Topeka market. Normal-ization allows one to see the relative changesin activity, or liquidity. We look at the relativeimportance of these measures next.

Component Weights in the REMI

Although each of these components cap-tures an important aspect of the market, it ismore di�cult to assign their weights in theindex, i.e.

. . . in the construction of index numbers . . .it is well known that there is not a single“true” index number of prices or outputs.This is because reality is multi-dimensionaland any attempt to express a multi-dimensional set as a simple number mustinvolve arbitrary assumptions.—Boulding(1958, p. 53)

Weights should allow components to movethe index without violating our intuition of therelative importance of each component. Thisintuition motivates my arbitrary assumptionsthat no one component dominate the REMIand that price have more weight than salesor DoM. I operationalize these assumptions

by putting more weight on price and by set-ting a goal that the REMI rise when two ofthree components indicate the market is hot-ter and fall when two of three indicate it iscooler. In the remaining discussion of weights,I will mention how well each weightingscheme does with respect to this Goal.Weighting schemes will be identi�ed by theirprice/sales/DoM weights, i.e., 50/30/20means that the REMI value is derived 50percent from price, 30 percent from salesand 20 percent from DoM.12 This arbitrarygoal may not be the best way to chooseweights, but it is fairly intuitive. It's obviouslypossible to use di�erent weights, set by thepreferences of the person who wants to usethe REMI, so this is merely an example. TheREMI only requires three streams of data, abaseline year and set of weights to be useful.The user can set the baseline year andweighting without a�ecting other users, aslong as they are not trying to compare REMInumbers. If people want to compare REMIsfrom di�erent markets, then they have toagree on the baseline year and weights. Thatprocess is beyond the scope of this article,but it will evolve under market, regulatoryand/or industry pressures for a REMI that isuseful. Let us proceed with these caveats inmind.

100/0/0: In this scheme price alonerepresents the market. Although price in-dices (including the CSI) do not claim tomeasure “the market,” they are ofteninterpreted that way. A 100/0/0 REMImisses the Goal 30 times, i.e., a REMI thatrises and falls with price alone falls whensales and DoM indicate the market is hotor rises when they indicate it is cold in 30of 119 monthly observations.

50/30/20: Price gets one half share, andsales get a bigger share than DoM. Sales



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get more weight than DoM because theyare the second most reported marketstatistic and a major component of liquid-ity, e.g., “summer selling season.” Thepragmatic reason to give more weight tosales is that it is less volatile than DoM,which can vary from 0 to 120 or moredays. 50/30/20 misses the Goal 11times.

40/40/20: This weighting is too light onprice, but we consider it here becausethese round numbers happen to coincidewith a weighting that is the inverse of thevariance of each component in MV data.13

40/40/20 misses the Goal 14 times.

33/33/33: A neutral weighting is intuitive,but many people prefer to give moreweight to price, and equal weights do notwork well when components have un-equal variance (see 40/40/20). Put an-other way, the component with more vari-ance will “drive” the REMI. 33/33/33misses the Goal 17 times.

Considering all these factors and thefundamentally arbitrary measure of indexweights, I discard 100/0/0 and 33/33/33schemes. I am uncomfortable with the 40/40/20 set of weights because it was derivedex-post from sample data and because itgives price and sales the same weight. I usethe 50/30/20 weights because it gives moreweight to price and the least weight to DoM,which is likely to have a high variation in mostmarkets.14

Constructing the REMI

The REMI-MV was constructed using thefollowing steps:15

1. Gather individual transaction data basedon �lter criteria (e.g., by city, ZIP code,number of bedrooms, tract, etc.) for asmany months as desired. I downloadeddata from about 19,000 sales thatclosed escrow between January 2000

and November 2009 in Mission Viejo.16

2. Calculate median price and DoM bymonth (t) to get values of pricet andDoMt. Also count the number of salesto get salest.

17 Figure 1 shows nominalstatistics for these variables.

3. Normalize all values to the base month(the �rst month in the series) by dividingeach variable by its base month valueto get indexed values, e.g.,

Figure 2 shows Mission Viejo data afterthey are normalized to 1.00 using basemonth values from January 2000.

4. Combine index values using weights toget the monthly REMI. Since the REMIrises with price and sales but falls withDoM, add the �rst two and subtractDoM, i.e.,

where multiplication by 100 and divisionby REMI1 makes the REMI an integerindex with a value of 100 in the basemonth. See Table 1 and Figure 3 forREMI-MV values and Figure 4 for REMIswith di�erent weights.



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Discussion

This section discusses the REMI's correla-tions with future REMI values and prices, andwhy the REMI gives a better description ofmarket conditions than price indices.

Although the REMI may be useful as abackward looking indicator of market activity,many people want to use it to look forward.Of course, they can already use the CSI tolook for future prices, but the 0.997 correla-tion between CSI values separated by threemonths means that anyone with a ruler can“predict” future CSI values. Can the REMIindicate market activity (REMI values) orprices in the future? The correlation betweenthe current REMI and the REMI for the nextmonth is 0.87; it falls to 0.76 and 0.65 at twoand three month distances, respectively. Thecorrelations between REMI values and pricesare initially disappointing (0.39 in the same

month and 0.41 between current REMI andthe next month's median price), but they im-prove with more time, i.e., the correlation be-tween current REMI and median price of two/three/four months later is 0.43/0.45/0.46.The REMI may be a reasonable indicator ofwhere prices are going.

Second, it is important to consider the big-gest problem with using prices to character-ize a market, i.e., their tendency to rise butnot fall. This downward stickiness is theresult of sellers who prefer to wait to get theirprice rather than sell at the market price.Many sellers do not enter the market (i.e.,sales fall) or exit the market as canceled,expired or withdrawn listings. These exitsare not included in the REMI (or other indi-ces), but—as Figure 5 shows—they are neg-atively correlated with sales (-0.40); they alsohave a -0.12 correlation with the REMI.



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With sticky prices, markets must adjustelsewhere, i.e., in lower sales or higher DoM.Compare Mission Viejo's market conditionsin February 2004 and February 2008: Al-though median prices are nearly identical($495,000 versus $500,000), sales andmedian DoM are nearly reversed (142 salesaveraging eight DoM in 2004 versus 70 salesand 148 DoM in 2008). REMI-MV valuesre�ect those di�erences: The 2004 REMI is229, but the 2008 REMI is only 15. Thesenumbers match the conventional wisdom(e.g., “Home prices still hot” in the February18, 2004 Orange County Register and “Tellus ‘Is home market at bottom?’ ’’ in theMarch 9, 2008 Orange County Register) anddemonstrate how the REMI provides a moreaccurate description of market conditionsthan indices based on price alone.

Conclusion

The real estate market su�ers from manystatistics and little understanding of how they�t together. The REMI combines values forprice, sales and days on market into a singleindex that can be used to understand thelevel and direction of market liquidity—evenrelative to other REMI-indexed markets.When the REMI is high, markets are hot;when the REMI is low, they are not.

References

Berkovic, J. A. and Goodman, J. L. (1996).Turnover as a Measure of Demand for Exist-ing Homes. Real Estate Economics, 24(4):421–440.

Boulding, K. E. (1958). The Skills of TheEconomist. Howard Allen Inc., Cleveland.

Case, K. E. and Shiller, R. J. (1987). Prices



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for Single-Family Homes Since 1970: NewIndices for Four Cities. New England EconomicReview, Sep:45–56.

Case, K. E. and Shiller, R. J. (1989). TheE�ciency of the Market for Single-FamilyHomes. American Economic Review, 79(1):125–137.

Clapp, J. M., Giaccotto, C., and Tirtiroglu,D. (1991). Housing Price Indices Based on AllT ransac t ions Compared to RepeatSubsamples. Real Estate Economics, 19(3):270–285.

Clayton, J., MacKinnon, G., and Peng, L.(2008). Time Variation of Liquidity in thePrivate Real Estate Market: An EmpiricalInvestigation. Journal of Real Estate Research,30(2):125–160.

Gatzla�, D. H. and Ling, D. C. (1994).Measuring Changes in Local House Prices:An Empirical Investigation of AlternativeMethodologies. Journal of Urban Economics,35(2):221–244.

Kluger, B. D. and Miller, N. G. (1990).Measuring Residential Real Estate Liquidity.Real Estate Economics, 18(2):145–159.

Krainer, J. (2001). A Theory of Liquidity inResidential Real Estate Markets. Journal ofUrban Economics, 49(1):32–53.

Murray, S. (2010). Housing prices remainweak. Wall Street Journal, 26 May.

Novy-Marx, R. (2009). Hot and ColdMarkets. Real Estate Economics, 37:1–22.

Standard & Poor's (2008). S&P/Case-Shiller Home Price Indices Methodology.Report. http://tinyurl.com/3v3ksb.

Stein, J. C. (1995). Prices and TradingVolume in the Housing Market: A Model with

Down-Payment E�ects. Quarterly Journal ofEconomics, 110(2):379–406.

NOTES:

1On 26 May 2010, the Wall Street Journal reported(emphasis added) “While the two home-price indexes[CSI and another] diverged in March, they outlined thesame overall trend of prices stabilizing after steepdrops during the recession. But with the expiration ofthe home-buyer tax credit likely to pull down demand,and the potential for more foreclosures, the housingmarket is still bouncing along at low levels.” (Murray,2010)

2DoM is the number of days between listing ahouse for sale and “selling” it, i.e., opening escrow. Theactual sale occurs after an escrow period (usually30-90 days) during which buyers and sellers ful�ll theprovisions of the sales contract. Monthly data are forsales that close escrow in that month.

3Clapp et al. (1991) argue that repeat sales are nobetter than all sales when measuring price changesover three or more years. Although this claim holds forlong term trends, it does not displace the CSI as themeasure of annual, quarterly or monthly trends. Gatzla�and Ling (1994) argue that hedonic and repeat salesbased indices need and waste too much data, respec-tively. They calculate indices based on fewer hedoniccomponents or assessed values, but their ideas havenot displaced the CSI.

4Using data from http://www2.standardandpoors.com/spf/pdf/index/cs�tieredprices�022603.xls.The correlation between MV price data and averageCSI values for Los Angeles and San Diego (MissionViejo lies between them) is 0.989.

5Note that both the probability of sale and DoMrely on actual sales; canceled listings are excluded.

6The positive relationship between price andcanceled listings may result from overshooting sellerexpectations, i.e., they list too high (and cancel) whenprices are higher.

7They claim that a fall in sales is correlated with anincrease in price dispersion. MV data fail to reject thishypothesis: A one percent increase in the standarddeviation of prices is correlated with a 0.48 percent fallin sales.

8The reverse is also possible: Fewer buyers leadsto a glut of sellers and houses and thus falling prices.

9In the inverse of this hypothesis, Krainer (2001)predicts that sellers will sell fast when prices are high(not waiting for the top of the market) but sell slowlywhen prices are low (hoping for a good match with abuyer). This hypothesis is also rejected by the data.

10Realtors use “days of inventory” (number ofhomes divided by sales per day) as a shorthand indica-tor of current market conditions. Sales and DoMreproduce this heuristic but only for closed sales.

11Because houses get stale as DoM rises, realtorscancel and relist houses as “new” properties to attract



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buyers with freshness (and often lower prices). I controlfor this behavior by adding DoM from previous canceledlistings to get the �nal DoM on the house that sold. I donot include cancelations, expirations and withdrawalsin the direct calculation of the REMI, but those “failures”do rise when the market cools; see Figure 5.

12Since the number of times the weighted REMImisses the goal is discrete and does not capture thedistance of the miss, it is very sensitive to weightchanges. This non-linear characteristic suggests that itwould be unwise to establish an optimal set of weightsbased on the “best” �t of a particular dataset. (Thebest �t for MV data—nine misses—occurs at 50/33/17.) It seems better to stick with “sensible” weights.

13Inverse weights are troublesome because theycan only be used when all of the data are available(weights will be inaccurate when new data are added),and they would be di�erent for every set of data—impeding comparison across REMIs. Dynamic weight-ing updates as variance-to-date changes, but it is veryunstable and misses the Goal more often.

14More weight on price can produce “good” results(e.g., 70/20/10 violates the goal 12 times), but suchheavy weights violate the notion that no one variableshould dominate the REMI.

15Data and calculations for this example are avail-

able at www.kysq.org/pubs/remi.xls.16Multiple Listing Service (“MLS”) data are avail-

able to members of the local board of realtors, who aregenerally real estate agents. I downloaded 19,833closed escrows from www.socalmls.com and deleted453 incomplete records (2.3 percent) to get a popula-tion of 19,380 sales. My data include about 1,000 salesfrom Ladera Ranch—a housing development foundedin 1999 that was included in Mission Viejo MLS datauntil sometime in 2006. Note also that the REMI su�ersfrom missing sales data (a problem common to existingprice indices), which reduces REMI volatility: In a hotmarket, the REMI is lower than it should be because itexcludes For Sale By Owner (“FSBO”) properties thatare sold without being recorded in the MLS. In a coolmarket, the REMI is higher than it should be becauseseller concessions to buyers do not appear in MLSsales prices. The omission of new housing sales fromthe REMI reinforces these biases: Builders sell volumeat full price in hot markets without listing on the MLS.In cold markets, they use the MLS and o�er ex-contract concessions to buyers.

17I do not adjust prices for in�ation. Although in�a-tion matters over decades, most people think of pricesin nominal dollars—often the price of their own home—and the REMI matches that heuristic.



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The Real Estate Market Index