Search Frictions and Idiosyncratic Price Dispersion in the US Housing Market Nadia Kotova 1 Anthony Lee Zhang 2 1 Stanford GSB 2 UChicago Booth
Prices of individual houses are highly volatile A large fraction of household wealth is in housing: US house ownership rate is 64.8%. Equity in own home constitutes 34% of the total net worth of the US population.
Prices of individual houses are highly volatile A large fraction of household wealth is in housing: US house ownership rate is 64.8%. Equity in own home constitutes 34% of the total net worth of the US population. Jordà, Schularick, Taylor (2019): Housing outperforms equity, same return (7%) but lower volatility (8% vs. 20%).
Prices of individual houses are highly volatile A large fraction of household wealth is in housing: US house ownership rate is 64.8%. Equity in own home constitutes 34% of the total net worth of the US population. Jordà, Schularick, Taylor (2019): Housing outperforms equity, same return (7%) but lower volatility (8% vs. 20%). But households do not hold a diversified real estate portfolio!
Prices of individual houses are highly volatile A large fraction of household wealth is in housing: US house ownership rate is 64.8%. Equity in own home constitutes 34% of the total net worth of the US population. Jordà, Schularick, Taylor (2019): Housing outperforms equity, same return (7%) but lower volatility (8% vs. 20%). But households do not hold a diversified real estate portfolio! Individual houses are subject to both volatility in average prices and large idiosyncratic risk.
Prices of individual houses are highly volatile A large fraction of household wealth is in housing: US house ownership rate is 64.8%. Equity in own home constitutes 34% of the total net worth of the US population. Jordà, Schularick, Taylor (2019): Housing outperforms equity, same return (7%) but lower volatility (8% vs. 20%). But households do not hold a diversified real estate portfolio! Individual houses are subject to both volatility in average prices and large idiosyncratic risk. Sources of housing idiosyncratic price dispersion (IPD) are not well understood.
This paper Search frictions are an important driver of housing IPD
This paper Search frictions are an important driver of housing IPD Empirical results: 1 PD is countercyclical and seasonal.
This paper Search frictions are an important driver of housing IPD Empirical results: 1 PD is countercyclical and seasonal. PD is strongly correlated with TOM and other market tightness measures.
This paper Search frictions are an important driver of housing IPD Empirical results: 1 PD is countercyclical and seasonal. PD is strongly correlated with TOM and other market tightness measures. Theory: 2 Construct a search-and-bargaining model to rationalize empirical results.
This paper Search frictions are an important driver of housing IPD Empirical results: 1 PD is countercyclical and seasonal. PD is strongly correlated with TOM and other market tightness measures. Theory: 2 Construct a search-and-bargaining model to rationalize empirical results. Calibrate model to quantify tradeoffs facing agents.
Outline Data 1 Measuring price dispersion 2 Empirical results 3 Model 4 Calibration 5 Conclusion 6
Data Corelogic (2001–2017) : Transaction prices & volumes, house characteristics Arms-length non-foreclosure transactions of single family residences with recorded sale price. Zillow Research (2010–2017) : County-month TOM, Zillow Home Value Index. Realtor.com (2012–2017) : Zip-month TOM. ACS Social Explorer (2012-2016) : Demographic covariates.
Measuring price dispersion: intuition log ( p ) i = 2 Zipcode mean i = 1 t 1 2 3
Measuring price dispersion: intuition log ( p ) log ( p ) i = 2 i = 2 Zipcode mean Zipcode mean i = 1 i = 1 t t 1 2 3 1 2 3
Measuring price dispersion For zip code z , house i w. characteristics x i , month t , estimate: log ( p it ) = η zt + γ i + f z ( x i , t ) + ǫ it
Measuring price dispersion For zip code z , house i w. characteristics x i , month t , estimate: log ( p it ) = η zt + γ i + f z ( x i , t ) + ǫ it � ǫ 2 it , error term, is our house-level measure of idiosyncratic PD
Measuring price dispersion For zip code z , house i w. characteristics x i , month t , estimate: log ( p it ) = η zt + γ i + f z ( x i , t ) + ǫ it � ǫ 2 it , error term, is our house-level measure of idiosyncratic PD Specification captures: η zt : Zipcode-month trend
Measuring price dispersion For zip code z , house i w. characteristics x i , month t , estimate: log ( p it ) = η zt + γ i + f z ( x i , t ) + ǫ it � ǫ 2 it , error term, is our house-level measure of idiosyncratic PD Specification captures: η zt : Zipcode-month trend γ i : Time-invariant house quality (observed or unobserved)
Measuring price dispersion For zip code z , house i w. characteristics x i , month t , estimate: log ( p it ) = η zt + γ i + f z ( x i , t ) + ǫ it � ǫ 2 it , error term, is our house-level measure of idiosyncratic PD Specification captures: η zt : Zipcode-month trend γ i : Time-invariant house quality (observed or unobserved) f z ( x i , t ) : Time-varying effects of characteristics x i
Measuring price dispersion For zip code z , house i w. characteristics x i , month t , estimate: log ( p it ) = η zt + γ i + f z ( x i , t ) + ǫ it � ǫ 2 it , error term, is our house-level measure of idiosyncratic PD Specification captures: η zt : Zipcode-month trend γ i : Time-invariant house quality (observed or unobserved) f z ( x i , t ) : Time-varying effects of characteristics x i Concerns: Time-varying effects of unobservables (e.g. construction quality, flood risk).
Measuring price dispersion For zip code z , house i w. characteristics x i , month t , estimate: log ( p it ) = η zt + γ i + f z ( x i , t ) + ǫ it � ǫ 2 it , error term, is our house-level measure of idiosyncratic PD Specification captures: η zt : Zipcode-month trend γ i : Time-invariant house quality (observed or unobserved) f z ( x i , t ) : Time-varying effects of characteristics x i Concerns: Time-varying effects of unobservables (e.g. construction quality, flood risk). Time-varying characteristics (e.g. renovations, depreciation).
� ǫ 2 � Distribution of it across zipcodes Mean: 16.8% SD: 4.6% P10: 11.3% P90: 22.6%
Summary of results IPD is countercyclical and seasonal In panel and cross-sectional specs, IPD is correlated with measures of market tightness: time-on-market, vacancy rates, migration rates, sales, prices
IPD is countercyclical and seasonal
IPD is countercyclical and seasonal
County-year panel regressions LogSD x 100 (1) (2) (3) (4) (5) (6) Log ZHVI − 1.056 ∗∗∗ − 0.834 (0.369) (0.768) Log sales − 0.971 ∗∗∗ − 1.750 ∗∗∗ (0.194) (0.541) Time on market (months) 0.521 ∗∗∗ 0.170 (0.162) (0.175) Vacancy rate 16.204 ∗∗∗ 10.804 ∗∗∗ (1.798) (2.407) Pop growth rate − 8.726 ∗∗∗ − 4.758 (2.057) (3.779) County fixed effects X X X X X X Year fixed effects X X X X X X Sample period 2000-2016 2000-2016 2010-2016 2007-2016 2007-2016 2010-2016 10,366 10,366 2,516 5,807 5,284 2,492 N Adjusted R 2 0.858 0.859 0.911 0.895 0.891 0.919
Zipcode cross-sectional regressions LogSD x 100 (1) (2) (3) (4) (5) (6) (7) Time on market (months) 2.463 ∗∗∗ 1.910 ∗∗∗ 2.941 ∗∗∗ 2.345 ∗∗∗ (0.088) (0.095) (0.120) (0.093) Vacancy rate 15.335 ∗∗∗ 7.486 ∗∗∗ 2.121 ∗∗∗ 4.412 ∗∗∗ (0.829) (0.852) (0.770) (0.757) Pop growth − 1.729 ∗ 0.401 − 1.130 ∗∗ − 1.095 ∗ (0.886) (0.783) (0.572) (0.639) Mean log price − 3.899 ∗∗∗ − 3.406 ∗∗∗ − 1.075 ∗∗∗ − 1.643 ∗∗∗ (0.209) (0.193) (0.222) (0.199) Controls X X X X X X X Fixed effects State CBSA N 4,109 4,109 4,109 4,109 4,109 4,109 4,109 Adjusted R 2 0.542 0.496 0.455 0.497 0.580 0.797 0.732
Robustness checks Zipcode-year panel regressions and county cross-sectional regressions. Controlling for time-between-sales and times sold. Removing polynomial term. Zillow vs Realtor.com time-on-market.
Model Stationary equilibrium search-and-bargaining model. 3 kinds of agents: Buyers: exogeneously enter market, match with sellers to buy houses 1 Matched owners: receive separation shocks at rate λ M 2 Sellers: match with buyers to sell and leave market 3
Model Stationary equilibrium search-and-bargaining model. 3 kinds of agents: Buyers: exogeneously enter market, match with sellers to buy houses 1 Matched owners: receive separation shocks at rate λ M 2 Sellers: match with buyers to sell and leave market 3 Price dispersion arises from dispersion in buyer match quality and seller holding costs
Agents and stationary flows Matched Sellers owners M S 1 − M S Buyers M B
Agents and stationary flows Matched ( 1 − M S ) λ M Sellers owners M S 1 − M S m ( M S , M B ) η B Buyers M B
Agents and stationary flows V M ( ǫ ) V S ( v ) Matched v ∼ F ( · ) Sellers owners M S 1 − M S ǫ ∼ G ( · ) Buyers M B V B
Agents and stationary flows V M ( ǫ ) V S ( v ) Matched v ∼ F ( · ) Sellers owners M S 1 − M S ǫ ∼ G ( · ) Trade condition: V M ( ǫ ) > V S ( v ) + V B Buyers M B V B
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