Frothy Housing Markets and Local Stock-Price Movements Christopher W. Anderson University of Kansas The School of Business, University of Kansas, 1300 Sunnyside Ave., Lawrence, KS, 66045-7585; Tel: 785-864-7340; Email: cwanderson@ku.edu. Eli Beracha East Carolina University College of Business, East Carolina University, 3129 Bate Building, Greenville, NC 27858-4353. Tel: 252-328-5824; Email: berachae@ecu.edu.

Abstract We investigate how conditions in residential real estate markets affect the pricing of stocks for companies with headquarters in metro areas across the United States. We find that stocks of firms headquartered in ‘hot’ residential real estate markets experience higher risk adjusted returns relative to stocks from ‘cold’ markets. We also find that stocks of firms located in hot real estate markets experience stronger return comovement with same-city stocks compared to stocks of firms located in cold markets. These conditional patterns in local stock prices are especially prevalent during the 1999 to 2004 period that coincides with the start of the most recent housing bubble. These findings suggest that shocks to households’ real estate wealth condition habitat effects observable in the pricing of local stocks.

Keywords Residential real estate · Wealth effects · Return comovement · Headquarters city effect · Household finance

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Introduction

Location matters in portfolio allocation decisions by investors. Specifically, U.S.

investors display a tendency to overweight local stocks when forming investment portfolios

(Coval and Moskowitz 1999; Huberman 2001; Zhu 2003). Additional evidence on this so-

called local bias has been found with respect to liquidity for stocks of firms located in rural

versus urban areas (Loughran and Schultz 2005), trading volume in cities affected by adverse

weather and ethnic holidays (Loughran and Schultz 2004), stock valuation of companies

headquartered in areas with few other local firms (Hong, Kubick, and Stein 2008), and

comovement among returns for firms that are headquartered near each other (Pirinsky and

Wang 2006; Barker and Loughran 2007; Anderson and Beracha 2008). In short, there are

discernible patterns in trading volume, prices, and returns of local stocks consistent with

habitat effects related to social interaction and sharing of information among local investors

who engage in correlated trading (Hong, Kubick, and Stein 2004, 2005; Ozsolyev 2005;

Barberis, Shleifer, and Wurgler 2005).

Our study is one of the few that investigates how observable attributes of securities

pricing are affected by price trends in residential real estate that may condition trading by

local investors. Because investments in housing are often the largest portion of household

wealth for a large segment of the population, this gap in our knowledge appears material.

Furthermore, rising home values in many markets combined with falling mortgage interest

rates were a major source of wealth and liquidity for households over the past several years

(Greenspan and Kennedy 2005). It is widely accepted that housing wealth stimulates

consumption (Case, Quigley, and Shiller 2003, 2005; Bostic, Gabriel, and Painter 2005;

Benjamin and Chinloy 2008). Economic theory suggests that home-related wealth conditions

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portfolio investment decisions by households (e.g., Palia, Qi, and Wu 2009) and recent

research shows that changes in home prices are positively related to local gross metropolitan

product (Miller, Peng, and Sklarz 2009). Consequently, it seems likely that during the recent

housing bubble rising prices in some areas of the United States and the liquidity provided by

opportunities to refinance and enjoy mortgage equity withdrawals have increased

discretionary investment activity and increased stock market participation.

In this paper we investigate how conditions in residential real estate markets affect

the pricing of local stocks for U.S. metropolitan areas over the fifteen-year period from July

1989 to June 2004. We use the well-established method of classifying stocks by the cities in

which firms are headquartered. We then classify cities according to trends in residential real

estate prices as measured by the repeat-sale home price indexes (HPIs) produced for

metropolitan statistical areas by the Office of Federal Housing Enterprise Oversight

(OFHEO). We then investigate whether extreme changes in home prices condition stock

returns in a manner consistent with local biases in stock trading activity. In particular, we

hypothesize that shocks to household wealth due to home price appreciation may stimulate

demand for stocks of locally headquartered firms and lead to higher than normal returns for

such stocks, a time series manifestation of Hong, Kubick, and Stein’s (2008) “only game in

town” story. We also hypothesize that rising housing wealth will be associated with a greater

degree of return comovement among local stocks to the extent that local investors subject to

shared housing-related wealth shocks invest in local stocks in response to similar information

and at similar times (Pirinsky and Wang 2006). Finally, we expect that the conditional

relations between local housing markets and local stock pricing will be most discernible

during the 1999-2004 portion of our sample period. This period coincides with the beginning

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of the most recent housing bubble and manifests a greater degree of dispersion of home price

movements across U.S. cities.

We find results consistent with our hypotheses. First, we find that stocks

headquartered in residential real estate markets categorized as ‘hot’ experience higher

monthly returns on average than stocks from ‘cold’ markets. These excess returns persist

after we adjust firm-level returns for exposure to risk factors. Second, we find that stocks of

firms located in hot real estate markets experience stronger return comovement with other

same-city stocks compared to stocks located in cold real estate markets. Finally, consistent

with our expectations these conditional patterns in stock returns are most pronounced during

the frothy housing markets period of 1999-2004. Overall, these findings suggest that

changes in investors’ real estate wealth affect trading activity and pricing of local stocks.

The remainder of this paper is organized as follows. The immediately following three

sections review evidence on the local bias among U.S. investors, section discusses extant

research on housing wealth and its effects on consumer and investor behavior, and motivates

our linking of changes in housing-related wealth to manifestations of local bias. We then

discuss our data and methods. Finally, we present empirical results and conclude.

Housing Wealth and Portfolio Investment Decisions

U.S. residential real estate market is valued at more than $20 trillion and is an asset

class held by about two-thirds of U.S. households. In recent years, residential real estate in

the United States experienced rapid price appreciation in some areas including parts of the

west coast, the east coast, Arizona, and Las Vegas where home prices nearly doubled within

a few years. At the same time, rent prices and personal income increased at a slower pace

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and thus did not provide fundamental support to housing price appreciation in such areas

(Case and Shiller 2003; Himmelberg, Mayer, and Sinai 2005). The price level of residential

real estate in these markets became a growing economic concern. This issue received

attention from the popular press that extensively referred to the situation as the ‘housing

bubble,’ as well as from the former Federal Reserve chairman, Alan Greenspan, who

described the level of housing prices in some areas as unsustainable in his testimony before

the Joint Economic Committee (Greenspan 2005). On the other hand, housing prices in some

parts of the U.S., e.g., Texas and Tennessee, were almost flat for the most of that time period,

resulting in large geographic dispersion in home price changes across the United States.

Since 2007 these patterns have reversed, as areas in which home prices rose most rapidly

have witnessed rapid price declines (Beracha and Hirschey 2009).

Since residential real estate is a widely held and often highly levered asset -- often the

largest component of an individual’s asset portfolio -- it affects personal consumption and

investment decisions in a material way (Benjamin, Chinloy, and Jud 2008). Bostic, Gabriel,

and Painter (2005) use combined data from the Survey of Consumer Finance and Consumer

Expenditure Survey and show that homeowners’ consumption increases with the value of

their homes. Using data from the United Kingdom, Campbell and Cocco (2005) confirm the

positive relation between housing wealth and consumption, and they show that this relation is

discernible at the regional level within a country. Case, Quigley, and Shiller (2005) find that

housing wealth has a large effect on household consumption in 14 developed countries

including the United States. They argue that the positive relation between housing wealth

and consumption results from relaxation of borrowing constraints through a second mortgage

or a line of credit that facilitate extraction of liquidity from housing equity. Slacalek (2006)

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shows similar cross-national results, and he finds that the effect of housing wealth on

consumption has increased over the past fifteen years, presumably because of easier access to

mortgage credit. The effect of changes in housing value on consumption behavior is greater

than the effect of changes in stock market wealth, in part because homeowners perceive

housing wealth to be more permanent (Benjamin, Chinloy, and Jud 2004a; Carroll, Otsuka,

and Slacalek 2006; Kishor 2007).

The wealth and risk associated with the volatile and illiquid nature of a house affects

not only consumption but also the level of risk homeowners are willing to bear from other

investments. Consequently, choices about investments in stocks and bonds are conditioned

by the household’s residential real estate holding and its overall risk tolerance (Flavin and

Yamashita 2002; Benjamin, Chinloy, and Jud 2004a; Yao and Zhang 2005; Piazzesi,

Schneider, and Tuzel 2007; Kullman and Siegel 2007). In particular, Cocco (2004) uses a

model of optimal portfolio and consumption decisions, parameterized by data from the Panel

Study of Income Dynamics, and finds that housing price risk and transaction cost crowds out

stockholdings, but he also shows that this effect is larger for low financial net worth. He also

shows that mortgage loan and stockholdings are positively related. Palia, Qi, and Wu (2009)

confirm Cocco’s (2004) result that background risks from housing (as well as from labor

income) decreases stock market participation, but also recognize that housing provides a

borrowing channel through which stock investments can be financed. Because of these

fundamental relations between wealth invested in residential real estate and wealth invested

in financial securities, integration in pricing between the two markets can often be detected

(Quan and Titman 1999; Jud and Winkler 2002; Kullman 2003; Cannon, Miller, and Pandher

2006; Anderson and Beracha 2009)

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The specific geographic location and pricing characteristics of the homeowner’s

primary residence introduces a natural and unavoidable local bias in his or her asset portfolio.

In the next section we review evidence that shows that even when opportunities for

geographic diversification of financial assets are easy to implement, investors still appear to

form portfolios with a bias toward stocks of locally headquartered companies.

Local Bias in Portfolio Investment

The tendency of investors to deviate from global diversification and instead hold

portfolios of financial assets that disproportionably overweight domestic securities is a

widely observed behavior (French and Porteba 1991; Kang and Stulz 1997; Keloharju and

Grinblatt 2001; Dahlquist, Pinkowitz, Stulz, and Williamson 2003; Chan, Covrig, and Ng

2005). Differences in political and economic systems, tax implications, language, culture,

and limited access to many foreign markets explain at least some of the home bias

phenomenon.

Even among investments within a country, resident investors hold portfolios that are

locally biased. Coval and Moskowitz (1999) find that U.S. money managers hold portfolios

of companies that are located about 10% closer to their offices, on average, than randomly

formed domestic portfolios. This local preference is particularly strong for small, highly

levered firms that produce non-traded goods, suggesting that information asymmetry is the

basis for the local biased behavior. Zhu (2003) observes local bias behavior among

individual investors that increases with firms’ advertising spending, consistent with the

familiarity hypothesis proposed by Huberman (2001).

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The existence of the local bias phenomenon within the United States appears to affect

stock trading volume, valuation, and patterns in returns. Loughran and Schultz (2004) show

that time zones associated with the city where companies’ headquarters are located affect

intraday trading. Similarly, they show that religious holidays and blizzards, events that affect

specific cities more than others, influence trading volume of companies headquartered in

those cities. Loughran and Schultz (2005) show that patterns in liquidity and price formation

differ for stocks of firms headquartered in rural versus urban areas. Hong, Kubik, and Stein

(2008) show that local bias has implications for stock prices in some regions via an “only

game in town” effect. Specifically, companies located in areas with relatively few firms per

capita are priced at a premium to companies that locate in regions with many firms per

capita.

Pirinsky and Wang (2006) and Anderson and Beracha (2008) show that stocks for

companies headquartered in the same city experience comovement in their returns. The level

of the local comovement of a stock is measured as the time-series sensitivity of its returns to

the return of an index of stocks from the same geographic area. This price comovement is

not explained by the market as a whole, industry classification, or regional macroeconomic

conditions. Similarly, Barker and Loughran (2007) show that pairwise correlations in returns

among stocks in the S&P 500 index increase with proximity of their headquarters locations.

The proximity effect among U.S. stocks may result from an investor habitat effect

manifest when groups of investors who share a literal or virtual common habitat concentrate

their attention on certain classes of securities (Barberis, Shleifer, and Wurgler, 2005).

Correlated trading by investors in a community may result in return comovement among

securities not otherwise attributable to underlying fundamentals or exposure to common risk

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factors. Correlated trading might be induced by privileged access to locally generated

information or by rumors or noise trading among local traders who share social networks

(Hong, Kubik, and Stein, 2004, 2005; Ozsolyev, 2005). The habitat effect is similar to a

category effect such as that demonstrated by Barberis, Shleifer, and Wurgler (2005), Kumar

and Lee (2006), and Ambrose, Lee, and Peek (2007).

Hypotheses

In spite of many studies on locally biased stock portfolios and the obvious local bias

in residential real estate investments, the effect of the geographically concentrated wealth

invested in housing on the homeowner’s locally biased stocks portfolio has not yet been

studied empirically. Our study expands the findings of Hong, Kubick, and Stein (2008) and

Pirinsky and Wang (2006) and also draws on the evidence provided by the literature on the

subject of housing wealth and investment decisions. We premise our hypotheses on the

assumption that household wealth related to rapid housing appreciation leads to more stock

market participation, in general, and triggers greater trading activity in local stocks, in

particular. We therefore expect that patterns in stock pricing frequently attributed to locally

biased trading activity will be more pronounced in areas with more positive changes in

residential real estate prices.

Our first hypothesis predicts that the returns on stocks of firms headquartered in areas

that experience rapid price appreciation are higher than returns on stocks of firms

headquartered in areas of lower price appreciation. Hong, Kubik, and Stein (2008) argue and

provide evidence that higher demand for local stocks in some areas pushes stock prices to a

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higher price level. If real estate wealth increases stock market participation and demand for

local stocks then we should observe higher returns for such stocks.

We next hypothesize that return comovement among same-city stocks, as

documented by Pirinsky and Wang (2006), is positively related to local residential real estate

market performance. Specifically, we expect to observe more positive return comovement

between stocks of firms headquartered in cities that experience rapid residential real estate

appreciation.

Finally, we hypothesize that the effect of residential real estate on portfolio returns

and return comovement is more pronounced during time periods with rapid appreciation in

the national residential real estate prices and greater dispersion in appreciation across cities,

such as the beginning of the housing bubble observed during the early 2000s. Our third

hypothesis is based on the fact that higher average national price appreciation is generally

associated with larger dispersion in price appreciation across U.S. cities. Consequently, we

anticipate home-price conditioned habitat effects to be more distinct during booming real

estate periods.

Data and Methods

Stock Price Data

We identify all firms with common stock price data available from the University of

Chicago’s Center for Research in Security Prices (CRSP) that also have company

headquarters data available from Standard & Poor’s COMPUSTAT database. As has

become the convention in the emerging literature on economic geography and finance, we

allocate each stock to a metropolitan statistical area (MSA) based on its headquarters location

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(Loughran and Schultz 2004; Hong, Kubick, and Stein 2004; Pirinsky and Wang 2006). We

discard stocks that cannot be assigned reliably to an MSA by headquarters location. In the

current version we also exclude all ADRs, REITs, and financial firms, although in earlier

versions we found that inclusion or exclusion of financial firms did not materially affect our

results. Finally, we exclude stocks without at least 36 months of return data available from

CRSP within a five-year period, and we also discard stocks associated with cities for which

the local set does not contain at least five firms throughout a relevant period of analysis.

Our classification of stocks to cities with HPI data results in samples of 85 cities and

1,927 headquartered stocks for the July 1989-June 1994 period, 98 cities and 2,563

headquartered stocks for the July 1994-June 1999 period, and 100 cities and 3,058

headquartered stocks for the July 1999-June 2004 period,. Table 1 summarizes the number

of stocks and associated headquarters cities, as well as the distribution of the number of

stocks per city, over each of our three five-year sample periods. Clearly, there are some

heavily firm-populated cities, but most cities have a sufficiently small number of firms that

local investors are likely to be familiar with these stocks from daily newspaper reading and

socializing in the community.

For each city we calculate the monthly return on an equally weighted portfolio of

locally headquartered firms (i=1,….N) as follows:

∑=

=N

i

titHQcity N

RR

1

,, (1)

We categorize these city-specific portfolios based on trends in local residential real

estate prices. We then investigate whether real estate trends condition excess stock returns.

In particular, these data also allow us to see if local price pressures might induce abnormal

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returns, consistent with the findings of a local value effect in Hong, Kubick, and Stein

(2008).

Also, as per Pirinsky and Wang (2006), for each stock assigned to a specific city we

compute the return on an equally-weighted portfolio of all other stocks for that city,

excluding the firm in question. In other words, each stock is assigned a unique local

portfolio of other stocks located within the same city, and for any month that portfolio’s

return is computed as follows:

∑≠=

− −=

N

jii

titjHQcity N

RR

1

,, 1 (2)

where Ri,t is the return in month t on another stock in the same city as stock j, and there are a

total of N such stocks (including stock j) in the city.

For each sample firm we also identify its industry using the industry classification

scheme of Kenneth French and obtain the corresponding monthly industry return in excess of

the risk-free rate (RIND-RF) from his data library. We also rely on French to obtain the

monthly market return over the risk free rate (RM-RF), high-minus-low book to market

portfolio return (HML), small-minus-big portfolio return (SMB), momentum factor return

(UMD), and the monthly risk-free rate (RF). These data allow us to follow the methods of

Pirinsky and Wang (2006) and Anderson and Beracha (2008) to investigate stock return

comovement among proximately headquartered firms. These studies document that

sensitivity to movements in other local stocks is strong and positive, a result suggestive of

local factors in price formation attributable to local bias.

Home Price Data

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To identify trends in local real estate prices, we rely on the home price indexes (HPI)

estimated and published by the Office of Federal Housing Enterprise Oversight (OFHEO).

OFHEO systematically collects all sale price data for homes from Freddie Mac and Fannie

Mae. OFHEO applies the weighted repeat sales method developed by Case and Shiller

(1989) to produce these indexes on a quarterly basis for 379 metropolitan statistical areas

(MSAs), including the cities for which we were able to assign firm’s on the basis of their

headquarters location. We subsequently refer to these MSAs as “cities” even though some

MSAs comprise multiple communities, including some MSAs that cross state borders, such

as Kansas City or the Quad Cities. Calhoun (1996) provides a description of the OFHEO’s

procedures in calculating the HPI, and there are several methodological issues surrounding

the construction and use of repeat sales price home price indexes (e.g., Clapham, Englund,

Quigley, and Redfearn 2005). For our purposes, we merely assume that city-specific price

patterns manifest in the HPIs are discernible by local investors or otherwise proxy for

perceptions about home equity values.1

We next classify cities by house price changes as measured by the HPIs. Specifically,

for three five-year periods – July 1989 through June 1994, July 1994 to June 1999, and July

1999 to June 2004 – we sort cities by the cumulative change in the city-specific HPIs.

Specifically, within each five-year period we categorize cities as “hot” (the top 30% by HPI

growth), “medium” (the middle 40% by HPI growth), and “cold” (the bottom 30% by HPI

growth).

Table 2 provides some summary statistics on HPI growth across sample MSAs for

each sample period. Mean and median HPI growth increases over time, as does dispersion

1 Bucks and Pence (2005) compare household survey data to mortgage transaction data and find that homeowners, on average, have accurate perceptions of home values.

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across MSAs as measured by standard deviation. In particular, the spread between HPI

growth between the hot markets and cold markets increases, with five-year growth rates in

hot markets exceeding that of cold markets by more than 50% during 1999-2004.

Conditioning Stock-Price Movements on Housing Market Trends

We first investigate whether local housing market trends affect performance of local

stocks. For each sample stock j we estimate the following four-factor model of monthly

returns across time:

( ) t,jtjtjtjt,Ft,Mjjt,Ft,j UMDmHMLhSMBsRRRR ε++++−β+α=− (3)

where for each month t

Rj, – RF = return on stock j in excess of the short-term risk-free rate,

RM–RF = return on the value-weighted market return in excess of the short-term risk-free rate,

SMB = the ‘small minus big’ factor, i.e., returns to small firms in excess of returns to large firms,

HML = the ‘high minus low’ factor, i.e., the returns on ‘value’ firms in excess of ‘growth’ firms,

UMD = the ‘momentum’ (‘up minus down’) factor, i.e., returns on previously high returning stocks minus low returning stocks

We compare the mean coefficient estimates from equation (3) across sample stocks

associated with hot, medium, and cold real estate markets in each five-year sample period.

Our primary hypothesis is with respect to excess risk adjusted stock returns as measured by

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the intercept coefficient for equation (3). Specifically, we hypothesize that αHOT > αMEDIUM

> αCOLD. For completeness, we also report how the non-intercept factor sensitivities differ

across real estate market conditions, allowing us to see if stocks in hot real estate markets

have differential betas or factor sensitivities with respect to the size, value, and momentum

factors.

We next investigate whether local real estate conditions affect return comovement

among local stocks. As per Anderson and Beracha (2008), for each sample stock j we

estimate equations such as the following:

( ) tjtFtMjjtFtj SMBsRRRR +−+=− ,,,, βα

( ) tjtFtjHQcityjtjtj RRUMDmHMLh ,,, ελ +−+++ − (4)

where λj is a measure of firm j’s return comovement with returns on the portfolio of other

stocks in the same headquarters city as stock j (Rcity-j,t as defined in eq. (2)) while controlling

for market, size, book-to-market, and momentum factors.2 We predict greater local

comovement, on average, among stocks in cities which have experienced unusually large

increases in local home prices. Specifically, we hypothesize that on average λHOT

>λMEDIUM>λCOLD.

Empirical Results

Housing Market Conditions and Local Stock Returns

2 Anderson and Beracha (2008) demonstrate that estimates of λj are materially lower when conditioned on a multiple factor model that includes size, value, and momentum effects. Estimates for λj based on a market model or a model that also includes industry portfolio returns, as used in Pirinsky and Wang (2006), are higher.

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Table 3 documents monthly raw returns on stocks associated sorted by whether they

are headquartered in hot, medium, or cold real estate markets for each of our five-year

sample periods. In the latter two sample periods higher average return is associated with

stocks headquartered in the hot areas, while stocks headquartered in the cold areas yield

lower average returns. Specifically, monthly returns on stocks from hot cities exceed those

from cold cities by twelve basis points per month, or a cumulative amount of about 1.4% per

year for both these latter periods. In contrast, housing market conditions do not appear to

condition raw returns on local stocks for the first 1989-1994 sample period, and the

difference in returns between hot market firms and cold market firms is a statistically

insignificant six basis points per month.

Results in Table 3 are for raw returns. One might expect that risk factors differ

systematically for firms across locations, for example due to industry clustering of growth

stocks in areas such as California (Anderson and Beracha 2008). We next attempt to adjust

for equity risk factors. Specifically, Table 4 shows the mean coefficient estimates from

security-specific factor regressions as per equation (3). In this table, we concentrate mainly

on the mean α coefficient estimates, which capture the excess return for each sample stock

after controlling for the Fama-French three risk factors and the Carhart momentum factor.

Nevertheless, the coefficient estimates for the equity risk factors suggest some systematic

differences across cities characterized by residential real estate trends.3

3 Specifically, in the 1989-1994 period sensitivity to the market risk factor and the small-minus-big (SMB) factor both appear to be inversely related to real estate price trends. In the 1994-1999 period stocks of firms from hot real estate markets appear to have negative exposure to the high-minus-low (HML) book-to-market factor. Finally, in the 1999-2004 period hot market firms appear to have more positive exposure to market risk, more positive exposure to the size factor, and more negative exposure to the book-to-market factor. Curiously, in no sample period does headquarters city home price appreciation appear to condition exposure to the up-minus-down (UMD) momentum factor.

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Consistent with average raw returns reported in Table 3, Table 4 reports no material

difference in risk adjusted returns for the 1989-1994 sample period. In contrast, for the latter

two periods we find that risk adjusted monthly returns -- as measured by α -- increase from

cold to medium to hot residential real estate markets of headquarters cities. In particular, for

1994-1999 difference between mean HOTα and COLDα is nearly 24 basis points per month, or

nearly 2.8% annually. For 1999-2004 the difference between mean HOTα and COLDα is

nearly 61 basis points per month, or about 7.3% annually. Again, the latter period is the one

associated with the highest average rates of home price appreciation and the greatest degree

of cross-sectional dispersion across sample cities.

In Table 5 we regress estimates of excess return measured by α , as per equation (3),

on a series of city-specific economic indicators and change in home prices. We run four

different regression specifications for each of the three predetermined five-year time periods.

In the first specification we control for population, number of firms, investment and non

investment per capita income, and change in home prices. The second specification controls

for the same factors except that the population and number of firms factors are replaced by

their ratio, which serves as a proxy for local firm density. The third and fourth specifications

are similar to the first and second specifications, respectively, but also control for changes in

population and per capita income through the subsample period. By regressing α on local

economic factors and change in local housing prices we are able to isolate the effect of home

prices on excess returns from other economic factors.

The results of Table 5 show that while each of the economic factors is statistically

significant at least during one of the three sample periods, none of the factors consistently

demonstrate statistical significance during the three time periods. The %Δ HPI coefficient,

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while mostly not statistically significant, is gradually increasing in magnitude through time.

The higher value of the %Δ HPI coefficient during the periods associated with rapid home

prices supports our results from Table 4 and provides additional evidence that excess risk

adjusted return is positively related to change in home prices even after controlling for local

economic factors.

Housing Market Conditions and Return Comovement among Local Stocks

Table 6 presents the estimated results for equation (4), which tests for local

comovement while controlling for the Fama-French three risk factors and Carhart momentum

factor. Specifically, for each firm-specific return series we extend the factor model of returns

as per Pirinsky and Wang (2006) to include the monthly return on each firm’s unique

headquarters city portfolio. The coefficient on this headquarters city factor measures the

degree of comovement in returns among same-city firms.

Overall, the results in Table 6 provide evidence that supports our second hypothesis

for the latter two sample periods. The λ coefficients for the July 1999 to June 2004 and the

July 1994 to June 1999 periods satisfy the HOTλ > MEDIUMλ > COLDλ condition, suggesting that

local comovement is indeed stronger in areas that experience greater residential real estate

appreciation. The difference between the λ coefficients is also statistically significant. The t-

statistics associated with the test COLDHOT λλ = are 6.59 and 3.60 for the later and the middle

period respectively. In contrast, for the period of July 1989 to June 1994 when home price

appreciation is lower on average and less widely dispersed across cities we find no

significant difference between the λ coefficients for the different real estate market

conditions. It is also important to note from Table 6 that inclusion of the headquarters city

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return factor results in attenuation of the average alpha coefficients in all sample periods.

This suggests that returns are indeed higher among firms located in hot real estate markets

(as shown in Table 4), but not after controlling for the comovement in returns experienced by

other firms in the same city.

Table 7 provides additional evidence on the determinants of return comovement

among same-city firms by regressing estimates of λ (from equation (4)) on a series of city-

specific economic indicators and change in home prices. We run four different regression

specifications for each predetermined five-year time period, which are identical to the

specifications presented in Table 5. Regressing λ on local economic factors and change in

housing prices allows us to observe the effect of home prices on local stock-price

comovement while controlling for other economic factors that may contribute to the return

comovement among same-city firms.

The results of Table 7 show that population is negatively related to λ while the

number of local firm has positive relation with λ. These two coefficients are statistically

significant in all the three five-year sample periods and suggest that return comovement

among same-city stocks is stronger in cities with more firms and weaker in cities with larger

population. The firm density coefficient, defined as the ratio between population and number

of firms, is also negative and statistically significant throughout the sample period. On the

other hand, the coefficients of investment and noninvestment per capita income as well as

changes in population and per capita income do not demonstrate consistent relation with λ

that has statistical significance throughout the sample period. %Δ HPI, which is the main

coefficient of interest in this table, is mainly positive, but not statistically significant during

the July 1989-June 1994 period. However, during the July 1994-June 1999 period the %Δ

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HPI coefficient is positive and statistically significant. The %Δ HPI coefficient demonstrates even

higher magnitude and statistical significance during the July 1999-June 2004 period. The

gradual increase in the value of the %Δ HPI coefficient during an increasingly positive

housing market supports our results from Table 6. It also provides additional evidence that

return comovement among same-city firms is stronger during periods of rapid housing

appreciation even after controlling for local economic factors.

Conclusion

We investigate how conditions in local residential real estate markets affect the

pricing of local stocks for U.S. metropolitan areas over the fifteen-year period from July

1989 to June 2004. We classify stocks by the city in which the firm is headquartered and

then partition cities according to trends in residential real estate prices as measured by the

repeat-sale home price indexes (HPIs) produced for metropolitan statistical areas by the

Office of Federal Housing Enterprise Oversight (OFHEO). We then investigate whether

extreme changes in home prices in these headquarters cities affect stock returns in a manner

consistent with local bias in stock trading and investment. In particular, we hypothesize that

large increases in household wealth due to home price appreciation may stimulate demand

for local stocks and lead to higher than normal returns. We also hypothesize that rising

housing wealth and habitat-effect trading will be associated with a greater degree of

comovement among returns of local stocks. We expect such effects to be most prevalent

during the 1999-2004 period characterized by greater dispersion in home price appreciation

across U.S. cities.

20

We find results consistent with our hypotheses, especially during the July 1999 to

June 2004 period that has been characterized both in the popular press and by scholars as the

start of the most recent housing bubble. We find that stocks located in ‘hot’ residential real

estate markets experience excess returns relative to stocks from ‘cold’ markets. We also find

that stocks of firms located in hot real estate markets experience stronger return comovement

with other same-city stocks compared to stocks located in cold real estate markets. Our

findings suggest that changes in investors’ real estate wealth affect trading activity and

pricing of local stocks.

21

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24

Table 1 Attributes of headquarters-city stock portfolios

Headquartered stocks per city Time period Stocks HQ cities Mean 25% Median 75% July 1989 to June 1994 1,927 85 22.7 9 15 34 July 1994 to June 1999 2,563 98 26.2 8 16 35 July 1999 to June 2004 3,058 100 30.6 8 16 41 This table shows the number of common stocks for CRSP/COMPUSTAT listed firms that are identified with their respective headquarters cities (metropolitan statistical areas) for each of three five-year time periods. We exclude financial firms, REITs, and ADRs, as well as stocks with fewer than 36 valid monthly returns within each five-year period. We also exclude cities with fewer than five headquartered firms.

Table 2 Residential real estate price trends across U.S. cities

Five-year change in home price index (HPI)

Time period HQ cities Mean Median Stdev. Hot markets

Cold markets

July 1989 to June 1994 85 15.6% 15.5% 12.1% 30.9% 2.3% July 1994 to June 1999 98 19.9% 19.6% 16.0% 37.0% 3.4% July 1999 to June 2004 100 40.3% 22.6% 24.9% 70.5% 19.2% This table shows the means, medians, and standard deviations of five-year cumulative changes in home price indices (HPI) for U.S. metro areas as published by OFHEO, as well as the mean changes for markets the hottest markets and the coldest markets. Hot (cold) market MSAs are those in the top (bottom) 30% of sample MSAs sorted by change in HPI within each five-year period. The summary statistics in this table represents only the MSAs for which we identify at least five companies with 36 or more months of data within each five-year time period.

25

Table 3 Raw monthly stock returns sorted by headquarter city HPI growth

Time period Cities (Stocks)

Mean (stdev.) % monthly stock returns

July 1989 to June 1994 Hot markets Medium markets Cold markets Hot minus cold (t-test)

25 (448) 35 (631) 25 (848)

1.23 (4.88) 1.29 (5.25) 1.17 (5.21)

0.06

(1.57)

July 1994 to June 1999 Hot markets Medium markets Cold markets Hot minus cold (t-test)

29 (942)

40 (1102) 29 (519)

1.70 (5.70) 1.45 (4.83) 1.58 (4.70)

0.12

(2.84)

July 1999 to June 2004 Hot markets Medium markets Cold markets Hot minus cold (t-test)

30 (1613) 40 (1088)

30 (357)

1.83 (8.34) 1.75 (6.98) 1.71 (6.26)

0.12

(2.12)

This table reports mean monthly stock returns for stocks whose headquarters can be identified from COMPUSTAT, for which there are at least five same-city stocks, and when there are at least 36 months of return data within a five-year period. Headquarters cities are categorized as hot, medium, or cold portfolios based on the cumulative change in the home price index (HPI) for each city for each five-year time period. Hot markets are among the top 30% of markets according to cumulative change in the city-specific housing price index (HPI), cold markets are the lowest 30%, and medium markets are the middle 40%.

26

Table 4 Mean coefficient estimates from factor models of monthly returns α

(t-stat) β

(t-stat) s

(t-stat) h

(t-stat) m

(t-stat) July 1989-June 1994 Hot RE Markets 0.472 0.855 0.887 0.325 -0.106 (5.04) (24.97) (14.77) (5.96) (-2.71) Medium RE Markets 0.540 0.889 0.949 0.323 -0.019 (7.23) (28.01) (16.62) (6.742) (-0.63) Cold RE Markets 0.501 0.956 1.112 0.247 -0.090 (6.81) (33.41) (22.49) (5.44) (-3.22) Hot minus Cold

0.029

(-0.23)

-0.101 (-2.16)

-0.225 (-2.78)

0.078 (1.04)

-0.016 (-0.35)

July 1994-June 1999 Hot RE Markets 0.700 0.773 0.796 -0.382 -0.255 (8.27) (28.87) (24.00) (-7.51) (-8.11) Medium RE Markets 0.565 0.849 0.673 0.085 -0.240 (6.43) (33.13) (22.70) (2.00) (-7.84) Cold RE Markets 0.461 0.783 0.718 0.100 -0.204 (5.75) (22.72) (17.72) (1.66) (-4.89) Hot minus Cold

0.239 (2.02)

-0.010 (-0.23)

0.078 (1.45)

-0.482 (-5.89)

-0.051 (-0.97)

July 1999-June 2004 Hot RE Markets 1.428 1.101 0.882 -0.090 -0.274 (22.16) (43.84) (33.63) (-3.04) (-14.86) Medium RE Markets 1.046 0.913 0.656 0.340 -0.220 (15.21) (34.77) (25.27) (11.69) (-12.00) Cold RE Markets 0.819 0.810 0.607 0.492 -0.217 (7.41) (21.09) (15.94) (9.51) (-7.23) Hot minus Cold

0.609 (4.16)

0.291 (5.16)

0.275 (4.69)

-0.582 (-8.65)

-0.570 (-1.38)

For stocks whose headquarters can be identified from COMPUSTAT, for which there are at least five same-city stocks, and when there are at least 36 months of return data for specified three five-year periods from July 1989 to June 2004, we estimate coefficients for the following stock return factor model (equation (3)):

( ) t,jtjtjtjt,Ft,Mjjt,Ft,j UMDmHMLhSMBsRRRR ε++++−β+α=− , where Rj is the monthly return on stock j, RF is the risk-free rate, RM is the value-weighted market return, SMB is the ‘small minus big’ size factor, HML is the ‘high minus low’ value factor, and UMD is the ‘up minus down’ momentum factor. Monthly stock returns are from CRSP. The remaining variables are obtained from Kenneth French’s data library available on the internet. Means of coefficient estimates and their respective t-statistics are reported in the table below according to whether a firm’s headquarters city is classified as a hot, medium, or cold residential real estate market for the specified five-year periods. Hot markets are among the top 30% of markets according to cumulative change in the city-specific housing price index (HPI) over the five-year period, cold markets are the lowest 30%, and medium markets are the middle 40%.

27

Table 5 Regressions of excess returns (α) on local economic factors including housing market conditions 1989-1994 1994-1999 1999-2004 (1a) (2a) (3a) (4a) (1b) (2b) (3b) (4b) (1c) (2c) (3c) (4c) Intercept 7.19

(3.00) 7.42

(3.17) 4.67

(1.51) 4.79

(1.56) 2.28

(0.90) 1.98

(0.78) 1.60

(0.56) 1.30

(0.45) 6.03

(2.37) 6.40

(2.53) 1.95

(0.64) 2.17

(0.71) Log(number of firms) 0.25

(2.21) 0.24

(2.07) 0.20

(1.61) 0.22

(1.78) 0.35

(3.00) 0.38

(3.18)

Log(population) -0.28 (-2.81)

-0.27 (-2.66)

-0.15 (-1.26)

-0.16 (-1.30)

-0.43 (-3.81)

-0.46 (-4.00)

Log(pop/no. of firms) -0.28 (-2.78)

-0.27 (-2.64)

-0.17 (-1.42)

-0.18 (-1.50)

-0.40 (-3.58)

-0.43 (-3.81)

Log(Noninvestment PCI) -1.45 (-2.00)

-1.60 (-2.50)

-1.35 (-1.71)

-1.52 (-2.22)

-0.48 (-0.78)

-0.24 (-0.44)

-1.27 (-1.72)

-0.89 (-1.36)

-0.24 (-0.39)

-0.65 (-1.20)

-0.32 (-0.52)

-0.71 (-1.32)

Log(Investment PCI) 0.53 (1.42)

0.57 (1.56)

0.61 (1.46)

0.65 (1.65)

0.65 (1.93)

0.61 (1.83)

0.92 (2.51)

0.84 (2.34)

0.35 (1.00)

0.54 (1.68)

0.65 (1.74)

0.85 (2.46)

%Δ Population 2.07 (1.56)

2.08 (1.57)

-0.09 (-0.07)

-0.12 (-0.10)

3.28 (2.43)

3.26 (2.42)

%Δ PCI -0.17 (-0.12)

-0.06 (-0.04)

2.37 (1.92)

2.18 (1.79)

0.61 (0.74)

0.78 (0.97)

%Δ HPI -0.10 (-0.28)

-0.07 (-0.20)

-0.27 (-0.67)

-0.26 (-0.64)

0.45 (0.90)

0.39 (0.78)

0.22 (0.40)

0.15 (0.28)

0.52 (1.96)

0.35 (1.48)

0.47 (1.69)

0.30 (1.19)

N 1927 1927 1927 1927 2561 2561 2561 2561 3057 3057 3057 3057 R-square 0.62% 0.39% 0.75% 0.74% 0.69% 0.66% 0.85% 0.80% 1.85% 1.78% 2.07% 2.00% For stocks whose headquarters can be identified from COMPUSTAT, for which there are at least five same-city stocks, and when there are at least 36 months of return data for specified three five-year periods from July 1989 to June 2004, we estimate coefficients for the following stock return factor model (equation (3)):

( ) t,jtjtjtjt,Ft,Mjjt,Ft,j UMDmHMLhSMBsRRRR ε++++−β+α=− , where Rj is the monthly return on stock j, RF is the risk-free rate, RM is the value-weighted market return, SMB is the ‘small minus big’ size factor, HML is the ‘high minus low’ value factor, and UMD is the ‘up minus down’ momentum factor. Individual stock returns are from CRSP. The remaining variables were obtained from Kenneth French’s data library available on the internet. We regress the stock-level estimates of excess return as measured by the individual jα̂ coefficients on a number of city-specific economic factors including housing-price data.

28

Table 6 Mean coefficient estimates for models of returns, including HQ- city factor α

(t-stat) β

(t-stat) s

(t-stat) h

(t-stat) m

(t-stat) λ

(t-stat) July 1989-June 1994 Hot RE Markets 0.378 0.696 0.715 0.247 -0.090 0.305 (3.90) (13.70) (10.98) (4.32) (-2.38) (7.27) Medium RE Markets 0.441 0.620 0.655 0.223 -0.011 0.323 (5.59) (13.20) (10.12) (4.53) (-0.37) (8.50) Cold RE Markets 0.368 0.636 0.750 0.147 -0.052 0.339 (4.93) (12.79) (12.11) (3.13) (-1.84) (7.68) Hot minus Cold

0.010 (0.08)

0.057 (0.78)

-0.035 (-0.36)

0.100 (1.31)

-0.038 (-0.84)

-0.034 (-0.49)

July 1994-June 1999 Hot RE Markets 0.403 0.371 0.415 -0.141 -0.144 0.494 (4.78) (8.14) (9.87) (-3.09) (4.50) (11.88) Medium RE Markets 0.440 0.586 0.483 0.047 -0.196 0.300 (5.98) (13.44) (12.85) (1.07) (-6.03) (7.27) Cold RE Markets 0.457 0.608 0.574 0.082 -0.183 0.242 (4.34) (11.18) (10.90) (1.35) (-4.25) (4.29) Hot minus Cold

-0.054 (-0.39)

-0.237 (-3.22)

-0.159 (-2.31)

-0.223 (-2.92)

0.039 (0.73)

0.252 (3.60)

July 1999-June 2004 Hot RE Markets 0.483 0.414 0.321 -0.088 -0.061 0.739 (6.82) (10.00) (9.47) (-2.84) (-3.13) (18.42) Medium RE Markets 0.546 0.520 0.374 0.168 -0.120 0.492 (7.73) (13.22) (12.22) (4.91) (-6.09) (13.30) Cold RE Markets 0.679 0.694 0.501 0.400 -0.182 0.164 (6.05) (14.63) (12.12) (7.28) (-6.04) (4.25) Hot minus Cold

-0.196 (-1.23)

-0.280 (-3.10)

-0.180 (-2.47)

-0.488 (-6.88)

0.121 (2.75)

0.575 (6.59)

For stocks whose headquarters can be identified from COMPUSTAT, for which there are at least five same-city stocks, and when there are at least 36 months of return data for specified three five-year periods from July 1989 to June 2004, we estimate coefficients for the following stock return factor model (equation (4)):

( ) ( ) tjtFtjHQcityjtjtjtjtFtMjjtFtj RRUMDmHMLhSMBsRRRR ,,,,,,, ελβα +−++++−+=− − , where Rj is the monthly return on stock j, RF is the risk-free rate, RM is the value-weighted market return, SMB is the ‘small minus big’ size factor, HML is the ‘high minus low’ value factor, UMD is the ‘up minus down’ momentum factor, and Rcity-j is the return on the equally-weighted portfolio of other stocks located in stock j’s headquarters city. Individual stock returns are from CRSP. The remaining variables were obtained from Kenneth French’s data library available on the internet. Means of coefficient estimates and their respective t-statistics are reported in the table below according to whether the headquarters city is classified as a hot, medium, or cold residential real estate market for the specified five-year periods. Hot markets are among the top 30% of markets according to cumulative change in the city-specific housing price index (HPI) over the five-year period, cold markets are the lowest 30%, and medium markets are the middle 40%.

29

Table 7 Regressions of local comovement (λ) on local economic factors including housing market conditions 1989-1994 1994-1999 1999-2004 (1a) (2a) (3a) (4a) (1b) (2b) (3b) (4b) (1c) (2c) (3c) (4c) Intercept 2.57

(1.93) 1.28

(0.98) 2.88

(1.68) 2.24

(1.31) 2.72

(1.91) 2.03

(1.43) 2.03

(1.26) 1.48

(0.93) -0.33

(-0.22) -0.88

(-0.59) 1.28

(0.71) 0.97

(0.54) Log(number of firms) 0.30

(4.73) 0.27

(4.21) 0.27

(3.89) 0.28

(3.99) 0.23

(3.37) 0.21

(3.00)

Log(population) -0.14 (-2.49)

-0.13 (-2.27)

-0.16 (-2.37)

-0.16 (-2.39)

-0.10 (-1.56)

-0.09 (-1.31)

Log(pop/no. of firms) -0.16 (-2.90)

-0.15 (-2.59)

-0.20 (-2.96)

-0.20 (-2.99)

-0.16 (-2.43)

-0.13 (-2.02)

Log(Noninvestment PCI) -0.32 (-0.79)

0.53 (1.49)

0.13 (0.44)

0.99 (2.60)

-0.32 (-0.93)

0.23 (0.75)

-0.56 (-1.37)

0.12 (0.32)

0.64 (1.78)

1.24 (3.90)

0.68 (1.87)

1.25 (3.93)

Log(Investment PCI) -0.30 (-1.46)

-0.51 (-2.50)

-0.50 (-2.16)

-0.74 (-3.36)

-0.17 (-0.92)

-0.26 (-1.41)

-0.06 (-0.31)

-0.20 (-0.98)

-0.56 (-2.74)

-0.85 (-4.46)

-0.69 (-3.13)

-0.98 (-4.80)

%Δ Population 0.79 (1.08)

0.74 (1.01)

0.43 (0.61)

0.37 (0.53)

-1.10 (-1.39)

-1.07 (-1.35)

%Δ PCI -1.97 (-2.46)

-2.57 (-3.26)

0.70 (1.02)

0.37 (0.55)

-0.46 (-0.97)

-0.72 (-1.52)

%Δ HPI 0.02 (0.08)

-0.15 (-0.81)

0.16 (0.71)

0.08 (0.37)

0.83 (2.90)

0.67 (2.39)

0.70 (2.31)

0.58 (1.94)

0.55 (3.57)

0.79 (5.65)

0.60 (3.66)

0.85 (5.84)

N 1927 1927 1927 1927 2561 2561 2561 2561 3057 3057 3057 3057 R-square 2.27% 1.19% 2.66% 1.83% 1.90% 1.41% 1.97% 1.44% 3.46% 3.02% 3.55% 3.15% For stocks whose headquarters can be identified from COMPUSTAT, for which there are at least five same-city stocks, and when there are at least 36 months of return data for specified three five-year periods from July 1989 to June 2004, we estimate coefficients for the following stock return factor model (equation (4)):

( ) ( ) t,jt,Ft,jcityjtjtjtjt,Ft,Mjjt,Ft,j RRUMDmHMLhSMBsRRRR ε+−λ++++−β+α=− − , where Rj is the monthly return on stock j, RF is the risk-free rate, RM is the value-weighted market return, SMB is the ‘small minus big’ size factor, HML is the ‘high minus low’ value factor, UMD is the ‘up minus down’ momentum factor, and Rcity-j is the return on the equally-weighted portfolio of other stocks located in stock j’s headquarters city. Individual stock returns are from CRSP. The remaining variables were obtained from Kenneth French’s data library available on the internet. We regress the stock-level estimates of comovement with same-city stocks as measured by the individual jλ coefficient estimates on a number of city-specific economic factors including housing-price data.