Do households use home-ownership to insure themselves? Evidence across U.S. cities

Abstract

Are households more likely to be homeowners when "housing risk" is higher? We show that home-ownership rates and loan-to-value (LTV) ratios at the city level are strongly negatively correlated with local house price volatility. However, causal inference is confounded by house price levels, which are systematically correlated with housing risk in an intuitive way: in cities where the land value is larger relative to the local cost of structures, house prices are higher and more volatile. We disentangle the contributions of high price levels from high volatilities by building a life-cycle model of home-ownership choices. We find that higher price levels can explain most of the lower home-ownership. Higher risk in the model leads to slightly lower home-ownership and LTV ratios in high land value cities. The relationship between LTV and risk is corroborated by LTV's negative correlation with price volatility in the data and highlights the importance of including other means of incomplete insurance in models of home-ownership.

The housing stock, housing prices, and user costs: the roles of location, structure and unobserved quality

“…Furthermore, we use repeated cross-sections sampled from periods of both housing price decline and boom in England and find that the estimated relationship between physical attributes, selection and rent-to-price remains very stable. We find that recent movements in the rent-to-price ratios of housing are reflected entirely in the value of land, 4 which is consistent with the methods and findings in Davis and Heathcote (2007), Davis and Palumbo (2008), and Amior and Halket (2014). Taking this one step further, if we assume that a single risk-adjusted discount rate prices all housing in the market as in Epple et al (2013), then we can say more.…”

“…Therefore most of the medium to high frequency variation in prices and rents over time in our data is captured by changes in the value of location (i.e. land) and not in the valuation of dwelling type or size, consistent with findings throughout the housing literature (Davis and Heathcote (2007); Davis and Palumbo (2008); Amior and Halket (2014)). …”

“…We study what aspects of housing units explain why some units are more likely to end up in the rental sector while others are more likely to end up in the owner-occupied sector. Glaeser and Shapiro (2003) and Amior and Halket (2014) observe that there is a strik-ing difference in the homeownership rates of single versus multi-family housing units throughout the US. This leads the former to note that "homeownership is particularly correlated with housing structure."…”

“…For instance, if we make the extreme assumption that r r − g r = r o − g o , equation (11) In Figures 10-11, we calibrate c o = .017 (consistent with measures of owner-occupied depreciation in Gatzlaff et al (1998);Malpezzi et al (1987); Amior and Halket (2014), among others) and set r − g = 0.01 for both sectors. For each estimate of ω 33 , we then plot the conditional and unconditional mean values of c r as functions of location, size, and dwelling type.…”

Moving to a Job: The Role of Home Equity, Debt, and Access to Credit

“…26 Amior & Halket (2011) consider a model which allows for house price levels to vary across cities but do not study mobility.…”