Online Appendix for. To Buy or Not to Buy: Consumer Constraints in the Housing Market

Similar documents
Table A.1: Use of funds by frequency of ROSCA meetings in 9 research sites (Note multiple answers are allowed per respondent)

Panel A: Treated firm matched to one control firm. t + 1 t + 2 t + 3 Total CFO Compensation 5.03% 0.84% 10.27% [0.384] [0.892] [0.

Guatemala. 1. Guatemala: Change in food prices

Appendix Table A1 Number of years since deregulation

Debt and Debt Management among Older Adults

This appendix tabulates results summarized in Section IV of our paper, and also reports the results of additional tests.

Online Appendix. for. Female Leadership and Gender Equity: Evidence from Plant Closure

The Financing and Growth of Firms in China and India: Evidence from Capital Markets

Volume 30, Issue 1. Gender and firm-size: Evidence from Africa

AJAE Appendix: Testing Household-Specific Explanations for the Inverse Productivity Relationship

Online Appendix for. Inattention and Inertia in Household Finance: Evidence from the Danish Mortgage Market,

Internet Appendix for CEO Personal Risk-taking and Corporate Policies TABLE IA.1 Pilot CEOs and Firm Risk (Controlling for High Performance Pay)

Gender and Firm-size: Evidence from Africa

Online Appendix to The Effect of Liquidity on Governance

Credit Supply and Monetary Policy: Identifying the Bank Balance-Sheet Channel with Loan Applications. Web Appendix

Internet Appendix to. The Price of Street Friends: Social Networks, Informed Trading, and Shareholder Costs. Jie Cai Ralph A.

Internet Appendix. For. Birds of a feather: Value implications of political alignment between top management and directors

Power and Priorities: Gender, Caste, and Household Bargaining in India

The Effects of Presidential Politics on CEO Compensation

Appendix A. Table A1: Marginal effects and elasticities on the export probability

Characteristics of Wine Consumers in the Mid-Atlantic States: A Statistical Analysis

This is a repository copy of Poverty and Participation in Twenty-First Century Multicultural Britain.

What does radical price change and choice reveal?

Labor Supply of Married Couples in the Formal and Informal Sectors in Thailand

The Role of Calorie Content, Menu Items, and Health Beliefs on the School Lunch Perceived Health Rating

Pitfalls for the Construction of a Welfare Indicator: An Experimental Analysis of the Better Life Index

A Web Survey Analysis of the Subjective Well-being of Spanish Workers

THE ECONOMIC IMPACT OF BEER TOURISM IN KENT COUNTY, MICHIGAN

Valuation in the Life Settlements Market

DETERMINANTS OF GROWTH

November K. J. Martijn Cremers Lubomir P. Litov Simone M. Sepe

Heat stress increases long-term human migration in rural Pakistan

Relationships Among Wine Prices, Ratings, Advertising, and Production: Examining a Giffen Good

Demographic, Seasonal, and Housing Characteristics Associated with Residential Energy Consumption in Texas, 2010

Online Appendix to. Are Two heads Better Than One: Team versus Individual Play in Signaling Games. David C. Cooper and John H.

Missing Data Treatments

Fair Trade and Free Entry: Can a Disequilibrium Market Serve as a Development Tool? Online Appendix September 2014

Post-Transaction Changes. Temporary Perfection. Possible Approaches

Multiple Imputation for Missing Data in KLoSA

PARENTAL SCHOOL CHOICE AND ECONOMIC GROWTH IN NORTH CAROLINA

Zeitschrift für Soziologie, Jg., Heft 5, 2015, Online- Anhang

Appendix A. Table A.1: Logit Estimates for Elasticities

Shopping on a Budget Schools Group Activity

Rural Vermont s Raw Milk Report to the Legislature

Investment Wines. - Risk Analysis. Prepared by: Michael Shortell & Adiam Woldetensae Date: 06/09/2015

Gasoline Empirical Analysis: Competition Bureau March 2005

Food Access Survey. (Interviewer Instructions: Do not read words written in parentheses ( ). They are the instructions for each question.

Sportzfun.com. Source: Joseph Pine and James Gilmore, The Experience Economy, Harvard Business School Press.

ASSESSING THE HEALTHFULNESS OF FOOD PURCHASES AMONG LOW-INCOME AREA SHOPPERS IN THE NORTHEAST

Perspective of the Labor Market for security guards in Israel in time of terror attacks

The Sources of Risk Spillovers among REITs: Asset Similarities and Regional Proximity

Eestimated coefficient. t-value

Coffee Price Volatility and Intra-household Labour Supply: Evidence from Vietnam

Fiscal Reaction Functions of Different Euro Area Countries

Characteristics of U.S. Veal Consumers

Handling Missing Data. Ashley Parker EDU 7312

Wine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts

Business Statistics /82 Spring 2011 Booth School of Business The University of Chicago Final Exam

How Rest Area Commercialization Will Devastate the Economic Contributions of Interstate Businesses. Acknowledgements

What are the Driving Forces for Arts and Culture Related Activities in Japan?

Weekly tax table with no and half Medicare levy

ECONOMIC IMPACT OF LEGALIZING RETAIL ALCOHOL SALES IN BENTON COUNTY. Produced for: Keep Dollars in Benton County

Citrus Attributes: Do Consumers Really Care Only About Seeds? Lisa A. House 1 and Zhifeng Gao

MBA 503 Final Project Guidelines and Rubric

Missouri State University

2016 China Dry Bean Historical production And Estimated planting intentions Analysis

Update to A Comprehensive Look at the Empirical Performance of Equity Premium Prediction

STA Module 6 The Normal Distribution

STA Module 6 The Normal Distribution. Learning Objectives. Examples of Normal Curves

Tips to enhance your wine tasting and investing experience

Table of Contents. Toast Inc. 2

Economics 101 Spring 2019 Answers to Homework #1 Due Thursday, February 7 th, Directions:

Consumers Favour Fairtrade as Ethical Label of Choice Fairtrade Ireland releases Fairtrade International annual report on Unlocking the Power

Climate change may alter human physical activity patterns

OF THE VARIOUS DECIDUOUS and

ARE THERE SKILLS PAYOFFS IN LOW AND MIDDLE-INCOME COUNTRIES?

Buying Filberts On a Sample Basis

THE ECONOMIC IMPACT OF MODEL WINERIES IN TEXAS. Industry Report

BORDEAUX WINE VINTAGE QUALITY AND THE WEATHER ECONOMETRIC ANALYSIS

ONLINE APPENDIX APPENDIX A. DESCRIPTION OF U.S. NON-FARM PRIVATE SECTORS AND INDUSTRIES

Costa Rica: In Depth Coffee Report: COFFEE INDUSTRY STRUCTURE

Predicting Wine Quality

Chicken Usage Summary

Economics Homework 4 Fall 2006

Curtis Miller MATH 3080 Final Project pg. 1. The first question asks for an analysis on car data. The data was collected from the Kelly

2017 FINANCIAL REVIEW

The premium for organic wines

Ex-Ante Analysis of the Demand for new value added pulse products: A

US Chicken Consumption. Presentation to Chicken Marketing Summit July 18, 2017 Asheville, NC

Economic and Fiscal Impacts of LiftFund:

FOOD ALLERGY CANADA COMMUNITY EVENT PROPOSAL FORM

Hamburger Pork Chop Deli Ham Chicken Wing $6.46 $4.95 $4.03 $3.50 $1.83 $1.93 $1.71 $2.78

Specialty Coffee Market Research 2013

Decision making with incomplete information Some new developments. Rudolf Vetschera University of Vienna. Tamkang University May 15, 2017

The cost of a healthy food basket

Religion and Innovation

Online Appendix to Voluntary Disclosure and Information Asymmetry: Evidence from the 2005 Securities Offering Reform

wine 1 wine 2 wine 3 person person person person person

The Bank Lending Channel of Conventional and Unconventional Monetary Policy: A Euro-area bank-level Analysis

Gail E. Potter, Timo Smieszek, and Kerstin Sailer. April 24, 2015

Transcription:

Online Appendix for To Buy or Not to Buy: Consumer Constraints in the Housing Market By Andreas Fuster and Basit Zafar, Federal Reserve Bank of New York 1. Main Survey Questions Highlighted parts correspond to version 1 / version 2; the version a respondent sees is randomly assigned (with 50/50 chance). Q1. Assume you had to move over the next 12 months. Please consider whether you would buy or rent your new home. Assume that you would qualify for a mortgage, but would need to make at least a 20% [5%] down payment if you choose to buy. So for instance, to buy a $100,000 home, you would have to put down at least $20,000 [$5,000]. Under these conditions, what is the percent chance that you would buy your new home (instead of renting it)? [ ] percent chance Q2. [same page, pops up after Q1 has been answered] Assume now that the minimum down payment was lower [higher]: 5% instead of 20% [20% instead of 5%] as in the previous scenario. So for instance, to buy a $100,000 home, you would have to put down at least $5,000 [$20,000]. Under these conditions, what is the percent chance that you would buy your new home (instead of renting it)? [ ] percent chance Q3. [same page, pops up after Q2 has been answered; same for everybody] Assume now that you just inherited a cash amount that is sufficient to cover the down payment for a home. You do not need to spend this money on the down payment if you do not want to you can use it for other purposes if you prefer. Under these conditions, what is the percent chance that you would buy your new home (instead of renting it)? [ ] percent chance

2. Variable Description & Statistics on Survey Respondents a. Variable description (omitting variables where the definition is obvious) Owner: = 1 if respondent owns (rather than rents) their primary residence. Numeracy: score from 0 (worst) to 5 (best) based on the number of correct answers to 5 questions testing respondents numeracy. Children < 18 in household: = 1 if respondent indicates that one or more children under the age of 18 live in their household (including those who are temporarily away). ln(median local HP): log of the median home price (HP) in a respondent s area (based on their zip code) as of August 2008, from Zillow.com. For 849 respondents, Zillow provides a matching zip code level median HP; for 49 without a zip code level HP, we use the county level HP, and for 164 the state level HP. Expected financial situation in 12 months: Based on the response to the question Do you think you (and any family living with you) will be financially better or worse off 12 months from now than you are these days? We group Much worse off and Somewhat worse off as the first category, About the same as the second category, and Somewhat better off and Much better off as the third category. E(rent inflation): Based on the question Twelve months from now, I expect the cost of renting a typical house/apartment to have increased by %. E(HPA next year): Based on the questions (i) Over the next 12 months, what do you expect will happen to the average home price nationwide? Over the next 12 months, I expect the average home price to... [ ] increase by 0% or more; [ ] decrease by 0% or more and (ii) By about what percent do you expect the average home price to [increase / decrease]? Please give your best guess. Over the next 12 months, I expect the average home price to [increase / decrease] by %. Know foreclosed household: = 1 if response is yes to Do you personally know any individuals/families that went into foreclosure since 2006? Local HPA volatility: Based on the standard deviation of annual county-level home price appreciation over the past ten years (2005-2015), using the CoreLogic house price index. For 108 respondents where no county-level index is available, we use the state-level index. Income: Based on the question Which category represents the total combined pre-tax income of all members of your household (including you) during the past 12 months? Please include money from all jobs, net income from business, farm or rent, pensions, interest on savings or bonds, dividends, social security income, unemployment benefits, Food Stamps, workers compensation or disability benefits, child support, alimony, scholarships, fellowships, grants, inheritances and gifts, and any other money income received by members of your household who are 15 years of age or older.

Savings: Based on the question Approximately what is the total current value of your [(and your spouse's/partner's), if they indicated they have one] savings and investments (such as checking and savings accounts, CDs, stocks, bonds, mutual funds, Treasury bonds), excluding those in retirement accounts? Equity: Based on the difference between self-assessed estimated market value of all homes owned by the respondent (and his/her spouse/partner) and the outstanding principal on all mortgages secured by these homes. Total home value comes from About how much do you think your home would sell for on today's market? and About how much in total do you think the other home(s) you own would sell for on today's market? [for respondents who own more than one home, or who rent their primary residence but own some other home]. Loans outstanding come from Approximately, what is the total amount of outstanding loans against your home(s), including all mortgages and home equity loans? Note in Table 1 in the paper and the pooled regressions below, we use an alternative measure of equity that is defined for current owners only, and uses only their primary residence. It is based on the questions If you sold your home today, would the proceeds be sufficient to pay off all mortgage loans and any costs of completing the sale? [Yes/No] and for those who respond yes, How much do you expect would be left after paying off your mortgage loans and any cost of completing the sale? This is then divided by the self-assessed home value to define the Equity 20% dummy (this dummy equals 0 for respondents who answer no to the question whether the proceeds from selling their home would be sufficient to pay off all mortgage loans and any costs of completing the sale). Non-housing debt: Based on the question Next consider all outstanding debt you (and your spouse/partner) have, including balances on credit cards (including retail cards), auto loans, student loans, other personal loans, as well as medical or legal bills, but excluding all housing related debt (such as mortgages, home equity lines of credit, home equity loans). Approximately, what is the total amount of your (and your spouse s/partner s) current outstanding debt? b. Descriptive statistics Note: some of the financial characteristics and other demographics are not available for all respondents, due to the fact that they come from a separate survey module that was fielded the same month but where not all of the same respondents participated. Additionally, some respondents skip single questions. In the descriptive statistics below, we only include respondents for which we have a particular variable. In the regressions, we add dummy variables for the cases where respondents did not answer a question, so that we do not lose observations; except if a variable is only missing for 1 or 2 observations, in which case we drop these respondents (4 total) since they would otherwise simply be dummied out.

Mean Std. Dev. Obs. Owner 0.70 1064 Age 50.86 15.29 1064 High school or less 0.11 1064 Some college / Associate's degree 0.35 1064 Bachelor's or higher 0.55 1064 Numeracy (0-5) 3.96 1.14 1064 Married 0.66 1064 Male 0.52 1064 Children under 18 in hh 0.29 1064 White 0.80 1063 Midwest 0.23 1064 Northeast 0.18 1064 South 0.36 1064 West 0.23 1064 Median local HP ($1000s) 256.94 239.63 1062 Local HPA volatility 0.08 0.04 1062 Personally know foreclosed hh 0.36 1064 Expected financial situation in 12 months: 1064 Worse 0.15 Unchanged 0.47 Improved 0.38 E(rent inflation) 1060 <4% 0.29 in [4%,10%) 0.32 10% 0.38 E(HPA next year) 1063 0% 0.10 in (0%,5%] 0.58 >5% 0.31 Income 1053 <40K 0.30 in [40K, 75K) 0.29 in [75K, 150K) 0.29 150K 0.12 Savings 993 <5K 0.43 in [5K, 30K) 0.23 in [30K, 100K) 0.15 in [100K, 500K) 0.15 >500K 0.04 Equity 1018 0 (incl. non-owners) 0.33 in (0, 75K] 0.18 in (75K, 150K] 0.15 in (150K, 300K] 0.18 >300K 0.16 Non-housing debt 1002 <1K 0.27 in [1K, 5K) 0.12 in [5K, 30K) 0.33 30K 0.28

3. Regression Results In the tables below, we provide two different sets of regression results: a. scenario-specific regressions of stated buying probabilities on individual characteristics. We report results from linear (OLS) regressions, but the code accompanying this article shows that results are robust to using fractional response regressions (which account for the fact that all responses are constrained to be in the [0,1] interval but are more difficult to interpret). The results in Table 2 in the main paper correspond to columns (2), (3), and (4) of the 0 percent scenario table. b. pooled OLS regressions that are analogous to the Changes columns in Table 1 of the main paper. a. OLS results, scenario by scenario 20 percent down payment: (1) (2) (3) (4) All All Owners Renters Owner 0.30 *** 0.09 (0.03) (0.07) Age/10^2 0.48 0.08-1.35 1.19 (2.38) (2.34) (3.28) (2.97) Age^2/10^4 0.89 0.73 3.90-2.05 (4.76) (4.70) (6.33) (6.18) Age^3/10^6-1.11-0.82-2.79 0.79 (3.04) (3.01) (3.92) (4.08) Some college/ad 0.07 * 0.06 0.08-0.02 (0.04) (0.04) (0.05) (0.05) BA or higher 0.13 *** 0.08 ** 0.13 ** -0.03 (0.04) (0.04) (0.05) (0.06) Numeracy (0-5) 0.03 *** 0.01 0.01 0.00 (0.01) (0.01) (0.01) (0.02) Married 0.10 *** 0.05 ** 0.05 0.01 (0.03) (0.03) (0.04) (0.04) Male 0.05 ** 0.03 0.04 0.01 (0.02) (0.02) (0.03) (0.03) Children<18 in hh -0.02-0.01-0.00-0.01 (0.03) (0.03) (0.04) (0.04) White 0.05 * 0.07 ** 0.17 *** -0.06 * (0.03) (0.03) (0.04) (0.04) ln(median local HP) 0.08 *** -0.00 0.00-0.01 (0.02) (0.02) (0.03) (0.03) Northeast 0.00 0.00-0.03 0.10 * (0.04) (0.04) (0.05) (0.06) South -0.01-0.01-0.00-0.01 (0.03) (0.03) (0.04) (0.04) West -0.01-0.01 0.01-0.05 (0.04) (0.04) (0.05) (0.06) Expect unchanged financial situation 0.04 0.03-0.00 0.08 **

(0.03) (0.03) (0.04) (0.03) Expect improved financial situation 0.07 ** 0.07 ** 0.06 0.10 ** (0.04) (0.03) (0.04) (0.04) E(rent infl.) in [4%,10%) -0.02 0.00 0.01 0.03 (0.03) (0.03) (0.04) (0.04) E(rent infl.) 10% -0.06 ** -0.02-0.05 0.07 (0.03) (0.03) (0.04) (0.04) E(HPA next year) in (0%,5%] -0.03-0.03-0.05 0.00 (0.04) (0.04) (0.05) (0.05) E(HPA next year) >5% -0.01-0.01-0.04 0.05 (0.04) (0.04) (0.05) (0.05) Know foreclosed hh 0.02 0.02 0.05-0.04 (0.02) (0.02) (0.03) (0.03) Local HPA volatility -0.50-0.15-0.53 0.93 ** (0.35) (0.32) (0.42) (0.46) Age 40 X Local HPA vol. 1.06 ** 0.59 0.79-0.15 (0.50) (0.46) (0.67) (0.66) Income in [40K, 75K) 0.04 0.07 0.03 (0.03) (0.04) (0.04) Income in [75K, 150K) 0.08 ** 0.12 ** 0.07 (0.04) (0.05) (0.06) Income 150K 0.20 *** 0.21 *** 0.34 ** (0.05) (0.06) (0.14) Savings in [5K, 30K) 0.07 ** 0.08 * 0.07 (0.03) (0.04) (0.04) Savings in [30K, 100K) 0.17 *** 0.19 *** 0.12 (0.04) (0.05) (0.07) Savings in [100K, 500K) 0.22 *** 0.21 *** 0.34 *** (0.04) (0.05) (0.09) Savings 500K 0.24 *** 0.24 *** 0.36 (0.06) (0.06) (0.29) Equity in (0, 75K] 0.06 0.09 (0.08) (0.08) Equity in (75K, 150K] 0.17 ** 0.19 ** Equity in (150K, 300K] 0.22 *** 0.23 *** Equity >300K 0.22 *** 0.22 ** Non-housing debt in [1K, 5K) -0.09 *** -0.10 ** -0.08 * (0.03) (0.04) (0.04) Non-housing debt in [5K, 30K) -0.07 ** -0.11 *** 0.04 (0.03) (0.04) (0.05) Non-housing debt 30K -0.12 *** -0.15 *** -0.03 (0.03) (0.04) (0.04) Start with 5% scenario -0.04 * -0.04 ** -0.03-0.06 * (0.02) (0.02) (0.03) (0.03) Constant -0.83 ** -0.12 0.01-0.15 (0.40) (0.39) (0.58) (0.49) Adj. R2 0.25 0.36 0.27 0.19 Obs. 1060 1060 743 317 Robust standard errors in parentheses. Significance: * p < 0.10, ** p < 0.05, *** p < 0.01

5 percent down payment: (1) (2) (3) (4) All All Owners Renters Owner 0.30 *** 0.13 (0.03) (0.08) Age/10^2 2.71 1.50 0.91 2.81 (2.44) (2.47) (3.19) (4.10) Age^2/10^4-4.10-2.27-1.01-4.99 (4.85) (4.91) (6.15) (8.45) Age^3/10^6 1.85 0.83 0.09 2.30 (3.08) (3.11) (3.80) (5.46) Some college/ad 0.07 0.06 0.09 * 0.01 (0.04) (0.04) (0.05) (0.08) BA or higher 0.11 ** 0.06 0.10 ** -0.01 (0.04) (0.04) (0.05) (0.08) Numeracy (0-5) 0.04 *** 0.02 ** 0.02 0.03 (0.01) (0.01) (0.01) (0.02) Married 0.12 *** 0.08 *** 0.08 ** 0.04 (0.03) (0.03) (0.03) (0.05) Male 0.01-0.00 0.02-0.03 Children<18 in hh -0.03-0.01-0.05 0.06 (0.03) (0.03) (0.03) (0.05) White 0.01 0.02 0.11 *** -0.07 (0.03) (0.03) (0.04) (0.05) ln(median local HP) 0.02-0.03-0.01-0.07 ** Northeast 0.05 0.05 0.01 0.11 (0.04) (0.04) (0.04) (0.07) South 0.02 0.01 0.02-0.02 (0.03) (0.03) (0.03) (0.06) West 0.01 0.02 0.03-0.04 (0.04) (0.04) (0.05) (0.08) Expect unchanged financial situation 0.05 0.04 0.04 0.03 (0.03) (0.03) (0.04) (0.06) Expect improved financial situation 0.11 *** 0.10 *** 0.07 * 0.16 ** (0.04) (0.03) (0.04) (0.07) E(rent infl.) in [4%,10%) 0.03 0.04 0.05 0.05 (0.03) (0.03) (0.03) (0.06) E(rent infl.) 10% -0.04-0.00-0.06 0.12 ** (0.03) (0.03) (0.04) (0.06) E(HPA next year) in (0%,5%] 0.02 0.01 0.00 0.01 (0.04) (0.04) (0.04) (0.06) E(HPA next year) >5% 0.04 0.04 0.05 0.02 (0.04) (0.04) (0.05) (0.07) Know foreclosed hh -0.00-0.01 0.03-0.09 ** Local HPA volatility -0.15 0.02-0.24 0.72 (0.34) (0.32) (0.38) (0.65) Age 40 X Local HPA vol. 0.60 0.24 0.36-0.23 (0.49) (0.49) (0.61) (0.83)

Income in [40K, 75K) 0.08 ** 0.07 0.13 ** (0.04) (0.04) (0.06) Income in [75K, 150K) 0.08 ** 0.08 0.12 (0.04) (0.05) (0.07) Income 150K 0.17 *** 0.15 *** 0.45 *** (0.05) (0.05) (0.14) Savings in [5K, 30K) 0.14 *** 0.15 *** 0.13 ** (0.03) (0.04) (0.06) Savings in [30K, 100K) 0.16 *** 0.19 *** 0.06 (0.04) (0.04) (0.09) Savings in [100K, 500K) 0.18 *** 0.19 *** 0.18 ** (0.04) (0.04) (0.09) Savings 500K 0.20 *** 0.21 *** 0.19 (0.06) (0.06) (0.32) Equity in (0, 75K] 0.05 0.09 Equity in (75K, 150K] 0.12 0.14 Equity in (150K, 300K] 0.17 ** 0.18 ** Equity >300K 0.14 * 0.15 (0.09) (0.10) Non-housing debt in [1K, 5K) -0.02 0.01-0.12 * (0.04) (0.04) (0.07) Non-housing debt in [5K, 30K) -0.01-0.02 0.03 (0.03) (0.04) (0.06) Non-housing debt 30K -0.04-0.02-0.05 (0.03) (0.04) (0.06) Start with 5% scenario -0.04 * -0.05 ** -0.02-0.08 * Constant -0.64-0.09-0.11 0.04 (0.42) (0.42) (0.56) (0.67) Adj. R2 0.22 0.27 0.18 0.17 Obs. 1060 1060 743 317 Robust standard errors in parentheses. Significance: * p < 0.10, ** p < 0.05, *** p < 0.01

0 percent down payment (hypothetical cash inheritance): (1) (2) (3) (4) All All Owners Renters Owner 0.18 *** 0.06 (0.03) (0.08) Age/10^2 7.10 *** 5.95 ** 5.51 * 8.59 ** (2.37) (2.42) (3.07) (3.99) Age^2/10^4-11.98 ** -9.97 ** -8.50-15.91 * (4.76) (4.86) (5.95) (8.33) Age^3/10^6 6.26 ** 5.11 4.12 8.41 (3.05) (3.12) (3.71) (5.40) Some college/ad 0.11 ** 0.10 ** 0.12 ** 0.07 (0.04) (0.05) (0.05) (0.08) BA or higher 0.10 ** 0.07 0.11 * 0.02 (0.04) (0.05) (0.05) (0.08) Numeracy (0-5) 0.04 *** 0.03 ** 0.03 ** 0.03 (0.01) (0.01) (0.01) (0.02) Married 0.08 *** 0.04 0.06 * -0.03 (0.03) (0.03) (0.03) (0.05) Male -0.03-0.03-0.00-0.08 * Children<18 in hh 0.03 0.03-0.01 0.18 *** (0.03) (0.03) (0.03) (0.05) White 0.02 0.02 0.06 0.01 (0.03) (0.03) (0.04) (0.05) ln(median local HP) -0.01-0.04-0.04-0.04 Northeast 0.02 0.02 0.00 0.11 (0.03) (0.03) (0.04) (0.06) South -0.00-0.01 0.01-0.01 (0.03) (0.03) (0.03) (0.06) West 0.03 0.04 0.06 0.04 (0.04) (0.04) (0.04) (0.08) Expect unchanged financial situation 0.03 0.02 0.01 0.03 (0.03) (0.03) (0.04) (0.07) Expect improved financial situation 0.08 ** 0.06 * 0.01 0.15 ** (0.04) (0.04) (0.04) (0.07) E(rent infl.) in [4%,10%) 0.04 0.05 * 0.05 0.08 (0.03) (0.03) (0.03) (0.06) E(rent infl.) 10% 0.01 0.04-0.01 0.17 *** (0.03) (0.03) (0.04) (0.06) E(HPA next year) in (0%,5%] -0.02-0.03-0.05 0.03 (0.04) (0.04) (0.04) (0.07) E(HPA next year) >5% 0.02 0.01 0.01 0.05 (0.04) (0.04) (0.05) (0.07) Know foreclosed hh 0.02 0.02 0.03 0.01 Local HPA volatility -0.36-0.33-0.43 0.04 (0.34) (0.33) (0.39) (0.67) Age 40 X Local HPA vol. 1.12 *** 0.92 ** 1.29 ** 0.04 (0.42) (0.42) (0.54) (0.68)

Income in [40K, 75K) 0.10 *** 0.06 0.17 *** (0.03) (0.04) (0.06) Income in [75K, 150K) 0.08 ** 0.06 0.13 ** (0.04) (0.05) (0.06) Income 150K 0.15 *** 0.13 *** 0.42 *** (0.04) (0.05) (0.11) Savings in [5K, 30K) 0.09 *** 0.10 *** 0.08 (0.03) (0.04) (0.05) Savings in [30K, 100K) 0.10 ** 0.12 *** -0.01 (0.04) (0.04) (0.09) Savings in [100K, 500K) 0.08 * 0.10 ** -0.04 (0.04) (0.04) (0.09) Savings 500K 0.13 ** 0.14 ** 0.04 (0.06) (0.06) (0.33) Equity in (0, 75K] 0.03 0.08 Equity in (75K, 150K] 0.07 0.10 Equity in (150K, 300K] 0.11 0.14 Equity >300K 0.08 0.08 (0.09) (0.09) Non-housing debt in [1K, 5K) 0.01 0.03-0.05 (0.04) (0.04) (0.08) Non-housing debt in [5K, 30K) 0.00-0.01-0.01 (0.03) (0.04) (0.06) Non-housing debt 30K 0.04 0.04 0.02 (0.03) (0.04) (0.07) Start with 5% scenario -0.06 *** -0.06 *** -0.05 ** -0.04 (0.02) (0.02) (0.02) (0.04) Constant -1.03 *** -0.66-0.65-1.04 * (0.39) (0.41) (0.56) (0.60) Adj. R2 0.13 0.15 0.09 0.26 Obs. 1060 1060 743 317 Robust standard errors in parentheses. Significance: * p < 0.10, ** p < 0.05, *** p < 0.01 Age effects The following chart shows the predicted effects of age in the three scenarios, using the coefficients from the first column in each of the three preceding regressions (that is, controlling for demographic characteristics and beliefs, but not for financial characteristics). The chart illustrates that the buying probabilities of young and middle aged respondents are more sensitive to a lower down payment requirement that those of older respondents.

Predicted pr(buy).2.4.6.8 25 30 35 40 45 50 55 60 65 70 75 Age Down payment 20% Down payment 0% Down payment 5% b. Pooled regressions The regressions in the following table pool the data across the three survey scenarios and contain a full set of respondent fixed effects. Thus, the dummies for the different down payment ( DP ) scenarios (with the 20 percent scenario as the omitted category) can be interpreted as the average withinrespondent difference in the stated buying probability across scenarios. Furthermore, by interacting these dummies with respondent characteristics, we can test to what extent the responses to changes in down payment requirements vary with these characteristics. The scenario dummies then become the average within-respondent difference for the omitted category of the interacting variable. For example, column (2) corresponds to the second and third rows of Table 1 from the main paper. The coefficients indicate that relative to the 20 percent down payment scenario, renters increase their probability of buying by 0.192 in the 5 percent scenario and by 0.406 in the 0 percent scenario. For owners, the changes are smaller: 0.155 (=0.192-0.037) for the 5 percent scenario and 0.226 (=0.406-0.180) for the 0 percent scenario.

Dependent variable: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Pr(buy) εε [0,1] All All All All All All Owners Owners Renters All DP = 0 percent 0.275 *** 0.406 *** 0.357 *** 0.388 *** 0.358 *** 0.332 *** 0.351 *** 0.344 *** 0.438 *** 0.528 *** (0.0140) (0.0300) (0.0311) (0.0257) (0.0255) (0.0234) (0.0358) (0.0332) (0.0413) (0.0376) DP = 5 percent 0.166 *** 0.192 *** 0.221 *** 0.216 *** 0.216 *** 0.183 *** 0.235 *** 0.217 *** 0.217 *** 0.283 *** (0.0107) (0.0224) (0.0240) (0.0205) (0.0197) (0.0177) (0.0298) (0.0276) (0.0314) (0.0303) DP = 0 X Owner -0.180 *** -0.0807 ** (0.0313) (0.0346) DP = 5 X Owner -0.0369 0.0218 (0.0236) (0.0272) DP = 0 X Age in [40,60) -0.0349-0.0131 (0.0362) (0.0356) DP = 0 X Age 60-0.186 *** -0.113 *** (0.0345) (0.0374) DP = 5 X Age in [40,60) -0.0382-0.0454 (0.0277) (0.0279) DP = 5 X Age 60-0.118 *** -0.107 *** (0.0271) (0.0315) DP = 0 X Savings 10k -0.207 *** -0.183 *** -0.164 *** -0.115 *** (0.0280) (0.0347) (0.0567) (0.0333) DP = 5 X Savings 10k -0.0829 *** -0.0930 *** -0.0557-0.0489 * (0.0217) (0.0278) (0.0416) (0.0268) DP = 0 X Nonh. debt<10k -0.157 *** -0.119 *** (0.0282) (0.0290) DP = 5 X Nonh. debt<10k -0.0918 *** -0.0695 *** (0.0215) (0.0228) DP = 0 X Income 75k -0.119 *** -0.0838 *** (0.0278) (0.0319) DP = 5 X Income 75k -0.0346-0.0365 (0.0215) (0.0268) DP = 0 X Equity 20% -0.188 *** (0.0366) DP = 5 X Equity 20% -0.119 *** (0.0294) Constant 0.441 *** 0.441 *** 0.441 *** 0.433 *** 0.436 *** 0.438 *** 0.549 *** 0.546 *** 0.178 *** 0.432 *** (0.00746) (0.00736) (0.00730) (0.00750) (0.00754) (0.00746) (0.00876) (0.00880) (0.0141) (0.00732) Nr respondents 1064 1064 1064 993 1002 1053 692 687 306 985 Standard errors in parentheses (clustered by respondent). All columns contain respondent fixed effects; columns (2)-(8) also control for DP scenario X question order effects (never significant). Significance: * p < 0.10, ** p < 0.05, *** p < 0.01