the determinants of buyer search duration in “hot” and “cold” residential real estate...

Presented by Ekaterina Chernobai page 1ERES 2011, Eindhoven 1

The Determinants of Buyer Search Duration in “Hot” and “Cold” Residential Real Estate Markets

Ekaterina Chernobai

• California State Polytechnic University, Pomona, U.S.A. College of Business Administration

Department of Finance, Real Estate, and Law• University of Nürtingen, Germany (Visiting Professor)

Department of Real Estate Management

Tarique Hossain

• California State Polytechnic University, Pomona, U.S.A.College of Business AdministrationDepartment of International Business and Marketing

Presented by Ekaterina Chernobai page 2

Idea

Housing liquidity

Time on the market Time to buy

Seller side Buyer side

We analyze:

• Determinants of search duration

• Differences in the effects in “hot” and “cold” housing markets

• Differences between private investors and non-investors

Also:

• Effects of “subprime crisis”

ERES 2011, Eindhoven


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Los Angeles MSA

Composite 10

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2004-2005 survey of recent house buyers

We run: 2007-2008 survey of recent house buyers

• Same geographic area in Southern California – most pronounced “bubble”• Same average house price in both time periods

January 2000 = 100



Idea

Geographical coverage of the surveys

San Luis Obispo county

Santa Barbara county

Ventura county

San Francisco

Los Angeles

San Diego


California


Idea

Some past studies of search duration:

• Case and Shiller (1988) “The Behavior of Home Buyers in Boom and Post-Boom Markets”

- House purchases during 1-year period

- Different geographic locations

- Some with rising some with declining prices

• Baryla, Zumpano, Elder (2000) “An Investigation of Buyer Search in the Residential Real Estate Market under Different Market Conditions”

- Houses purchased in different interest rate periods

- Different geographic locations (entire country)



Idea

• Krainer and LeRoy (2002) “Equilibrium Valuation of Illiquid Assets”

- Theoretical general equilibrium model of housing transactions

- Buyers’ valuations of houses are heterogeneous

- Weigh the search cost against the expected life-time utility less price

- Equilibrium: Longer expected stay in a house increases search duration

• E. Chernobai (working paper) “When Does Mobility Reduce Liquidity”

- Theoretical model that generalizes Krainer & LeRoy’s

- Short-term and long-term buyers

- Equilibrium: Long-term buyers search longer than short-term buyers



Research Questions

Questions of interest:

1.) Any effect of expected housing tenure on realized search duration?

2.) Are consumption-buyers different from investor-buyers?

3.) Any differences in 1.) and 2.) between “hot” and “cold” markets?

Our hypotheses:

Positive relationship if buy for consumption

Investors:- Time pressure to buy: Yes? No?- What does a typical consumption-

buyer want?

Short-term investors vs.Long-term investors

- Time to buy is shorter in “hot” market- Investors: Rel. proportion of short- &

long-term investors varies over time


Results


Survey 2004-2005 recent house-buyers

Mailed 6,000 questionnaires

Response rate: 11.3% (661)

Survey 2007-2008 recent house-buyers

Mailed 6,200 questionnaires

Response rate: 11.6% (719)

“Hot” market “Cold” market


Results


Variable Pooled “Hot” “Cold” Difference in means (p-value)Time To Buy 6.80 5.35 8.06 0.000***

First 0.24 0.25 0.23 0.538

Offers 1.78 1.83 1.74 0.279

New 0.09 0.07 0.10 0.076*

Miles 201.40 178.49 221.33 0.251

Price 612,625 602,267 621,900 0.284

Moving Up 0.32 0.32 0.32 0.987

Coast 0.38 0.40 0.36 0.336

Fixed 0.53 0.41 0.63 0.000***

HighLTV 0.34 0.33 0.35 0.504

1-5 years 0.34 0.41 0.27 0.000***

6-10 years 0.22 0.20 0.24 0.158

10+ years 0.37 0.31 0.42 0.000***

1-5 years *Investor 0.04 0.07 0.02 0.001***

6-10 years *Investor 0.01 0.01 0.02 0.281

10+ years*Investor 0.01 0.01 0.01 0.674

Investor 0.09 0.11 0.07 0.074*

Winter 0.16 0.06 0.25 0.000***

Spring 0.22 0.23 0.22 0.523

Summer 0.29 0.32 0.26 0.060*

Fall 0.32 0.38 0.27 0.001***

(months)

Correlation matrix:

no issues

DESCRIPTIVE STATISTICS


Results


Variable Pooled Hot market Cold marketIntercept 0.899 (<0.0001) 0.305 (0.1277) 0.291 (0.1440) -0.434 (0.1259) 0.887 (0.0008)

First 0.056 (0.5945) 0.051 (0.6236) 0.057 (0.5770) 0.373 (0.0089) -0.165 (0.2216)

Offers 0.218 (<0.0001) 0.212 (<0.0001) 0.221 (<0.0001) 0.195 (<0.0001) 0.280 (<0.0001)

New 0.476 (0.0009) 0.437 (0.0017) 0.445 (0.0010) 0.987 (<0.0001) 0.124 (0.4447)

Miles 0.000 (0.9190) 0.000 (0.7421) 0.000 (0.6030) 0.000 (0.0523) 0.000 (0.6554)

Price 0.000 (0.0054) 0.000 (0.0116) 0.000 (0.0120) 0.000 (0.0029) 0.000 (0.4476)

MovingUp -0.113 (0.2220) -0.150 (0.0991) -0.123 (0.1764) -0.284 (0.0306) 0.010 (0.9276)

Coast 0.236 (0.0040) 0.224 (0.0055) 0.227 (0.0046) 0.385 (0.0007) 0.227 (0.0323)

Fixed 0.027 (0.7399) -0.007 (0.9335) 0.012 (0.8768) 0.218 (0.0508) -0.200 (0.0595)

HighLTV -0.186 (0.0418) -0.143 (0.1163) -0.143 (0.1115) -0.186 (0.1783) -0.062 (0.5831)

1-5 years 0.621 (<0.0001) 0.528 (0.0009) 0.718 (0.0006) 0.405 (0.0614)

6-10 years 0.557 (0.0007) 0.560 (0.0006) 0.659 (0.0038) 0.487 (0.0247)

10+ years 0.866 (<0.0001) 0.877 (<0.0001) 0.803 (0.0002) 0.787 (0.0001)

1-5 years *Investor 0.540 (0.0060) 0.336 (0.1299) 1.028 (0.0034)

6-10 years *Investor -0.160 (0.6475) 0.617 (0.3038) -0.465 (0.2406)

10+ years*Investor -0.545 (0.1145) -0.197 (0.7039) -0.712 (0.0872)

Winter 0.351 (0.0031) 0.336 (0041) 0.315 (0.0065) 0.782 (0.0007) -0.030 (0.8263)

Spring 0.180 (0.0965) 0.172 (0.1027) 0.148 (0.1589) 0.274 (0.0519) 0.099 (0.4874)

Summer 0.259 (0.0102) 0.254 (0.0097) 0.247 (0.0110) 0.340 (0.0078) 0.105 (0.4400)

N obs. 806 806 806 375 431

Dependent variable:

logTimeToBuy

Weibull distribution

REGRESSIONRESULTS

consumption incentive


Results


We also looked at Pooled regression model with:

• “Hot” dummy interaction terms for all variables significant for many Splitting into “hot” and “cold” sub-samples is justified

• “Investor” dummy interaction terms for all variables significant for Tenure variables, Coast, and New

To identify consumption- and investment-driven submarkets, split “Hot” and “Cold” samples into

- Coastal zip code areas (43% of zip codes, 38% of obs.)

- Inland zip code areasERES 2011, Eindhoven

Results


Variable“Hot” (2004-2005) “Cold” (2007-2008)

Coastal Inland Coastal Inland

Intercept -0.378 (0.3663) -0.150 (0.6918) 1.099 (0.0107) 0.991 (0.0019)

First 0.508 (0.0663) 0.387 (0.0249) -0.612 (0.0118) -0.041 (0.8009)

Offers 0.176 (0.0097) 0.255 (<0.0001) 0.170 (0.0623) 0.323 (<0.0001)

New 0.297 (0.4324) 1.495 (<0.0001) 0.857 (0.0183) -0.009 (0.9608)

Miles 0.000 (0.0942) 0.000 (0.2333) 0.000 (0.2430) 0.000 (0.2254)

Price 0.000 (0.0984) 0.000 (0.1175) 0.000 (0.2863) 0.0000 (0.4855)

MovingUp -0.097 (0.6428) -0.348 (0.0331) -0.048 (0.7961) 0.188 (0.1797)

Fixed 0.281 (0.1264) 0.230 (0.0968) -0.074 (0.6780) -0.297 (0.0188)

HighLTV -0.644 (0.0116) 0.010 (0.9521) -0.189 (0.2999) -0.088 (0.5318)

1-5 years 0.986 (0.0028) 0.507 (0.0503) 0.192 (0.5651) 0.608 (0.0270)

6-10 years 1.059 (0.0027) 0.353 (0.2232) 0.229 (0.4749) 0.696 (0.0109)

10+ years 1.290 (<0.0001) 0.456 (0.0915) 0.965 (0.0014) 0.742 (0.0048)

1-5 years *Investor 0.419 (0.2046) 0.533 (0.0687) -0.930 (0.1218) 1.399 (0.0004)

6-10 years *Investor 0.189 (0.8648) 0.630 (0.3684) -0.962 (0.1176) -0.235 (0.6253)

10+ years *Investor 0.158 (0.8296) -1.258 (0.0740) -1.751 (0.0029) 0.039 (0.9429)

Winter 0.967 (0.0134) 0.554 (0.0689) 0.220 (0.3705) -0.239 (0.1256)

Spring 0.540 (0.0274) 0.106 (0.5366) 0.217 (0.3676) 0.093 (0.5740)

Summer 0.499 (0.0125) 0.104 (0.5301) 0.110 (0.6388) 0.119 (0.4519)

N obs. 149 226 157 274

Dependent variable:

logTimeToBuy

Weibull distribution

REGRESSIONRESULTS

consumption incentive

investment incentive - short term -

investment incentive

- long term -??? next housing cycle


Results

Presented by Ekaterina Chernobai page 13ERES 2011, Eindhoven

One possible explanation of differences between Coast & Inland results:

Hot Cold

Coast Inland Coast Inland

Variable Mean SD Mean SD Mean SD Mean SD

Search duration(months)

5.52 7.64 4.39 7.25 8.67 12.695 7.60 9.43

Purchase price($)

618,639 250,283 578,151 218,562 627,267 278,226 615,884 287,611

FRM financing (% of borrowers)

31.30 46.55 46.34 49.99 65.44 47.73 72.06 44.96

Conforming loans (% of borrowers)

12.35 32.90 15.15 35.86 36.73 48.21 33.98 47.36

Purchase price ($)

469,048 201,542 501,429 148,267 531,861 182,174 519,207 181,868


50.00 51.30 72.73 45.23 75.93 43.16 81.82 38.79

Non-conforming loans (% of borrowers)

87.65 32.90 84.85 35.86 63.27 48.21 66.02 47.36

Purchase price ($)

644,631 245,991 622,959 236,499 652,444 296,068 655,288 323,907


28.80 45.47 39.53 49.04 60.22 49.21 69.59 46.14

(means)

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Summarize


• Consumption-driven purchases: - “hot” market in coastal areas - “cold” market in inland areas

Long-horizon investment activity: - in just the opposite sub-markets

• Short-horizon investment activity: - in inland areas

Investment activity leads consumption activity


• The boom & burst of the “sub-prime” bubble affected search duration in Hot & Cold periods

the determinants of buyer search duration in “hot” and “cold” residential real estate...

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