Amenity‐Based Housing Affordability Indexes

The recent slump notwithstanding, substantial increases in house prices in many parts of the United States have served to highlight housing affordability for moderate‐income households, especially in high‐cost, supply‐constrained coastal cities such as Boston. In this article, we develop a new measure of area affordability that characterizes the supply of housing that is affordable to different households in different locations of a metropolitan region. Key to our approach is the explicit recognition that the price/rent of a dwelling is affected by its location. Hence, we develop an affordability methodology that accounts for job accessibility, school quality and safety. This allows us to produce a menu of town‐level indexes of adjusted housing affordability. The adjustments are based on obtaining implicit prices of these amenities from a hedonic price equation. We thus use data from a wide variety of sources to rank 141 towns in the greater Boston metropolitan area based on their adjusted affordability. Taking households earning 80% of area median income as an example, we find that consideration of town‐level amenities leads to major changes relative to a typical assessment of affordability.     

Risk and Return in the U.S. Housing Market: A Cross-Sectional Asset-Pricing Approach

This article carries out an asset-pricing analysis of the U.S. metropolitan housing market. We use ZIP code–level housing data to study the cross-sectional role of volatility, price level, stock market risk and idiosyncratic volatility in explaining housing returns. While the related literature tends to focus on the dynamic role of volatility and housing returns within submarkets over time, our risk–return analysis is cross-sectional and covers the national U.S. metropolitan housing market. The study provides a number of important findings on the asset-pricing features of the U.S. housing market. Specifically, we find (i) a positive relation between housing returns and volatility, with returns rising by 2.48% annually for a 10% rise in volatility, (ii) a positive but diminishing price effect on returns and (iii) that stock market risk is priced directionally in the housing market. Our results on the return-volatility-price relation are robust to (i) metropolitan statistical area clustering effects and (ii) differences in socioeconomic characteristics among submarkets related to income, employment rate, managerial employment, owner-occupied housing, gross rent and population density.     

Idiosyncratic Risk of House Prices: Evidence from 26 Million Home Sales

This paper uses about 26 million home sales to measure house price idiosyncratic risk for 7,580 U.S. zip codes during three periods: (1) when the U.S. housing market was stable (1996–2000), (2) booming (2001–2007) and (3) busting (2007–2012), and investigates the determinants of house price risk. We find very strong relationships between risk and some basic housing market characteristics. There is a U‐shaped relationship between risk and zip‐code level median household income; risk is higher in zip codes with more appreciation volatility; and risk is not compensated with higher appreciation.     

Housing Supply,Land Costs and Price Adjustment

We analyze relationships between housing supply elasticities, land costs and house price dynamics, contributing three main insights. First, higher housing supply elasticities help contain short‐run price spikes following demand shocks. Second, land price dynamics influence this relationship; supply responses are lessened and house price spikes are exacerbated as land prices increase. Third, we estimate a system of regional equations modeling housing supply using a Tobin's‐q specification (incorporating construction and land costs) and show that regional price dynamics are a function of the region's supply elasticity.     

Oil Prices and Urban Housing Demand

We develop a model of a monocentric, oil‐exporting city. The model predicts a “twist” (rotation combined with a level shift) of the house price gradient with an oil price change due to the combined producer price and transportation cost effects. Empirical findings support the predictions, with house price changes positively linked to the price of oil in cities specialized in oil and gas‐related industries, and negatively linked in suburban areas of all cities. These results quantify the large and differential risks to house prices associated with oil price changes both within and across cities. Overall, estimates suggest a 50% change in the price of oil results in a city‐wide house price change of 15% over five years in a city specialized in the production of oil (export employment share of 50%), whereas house prices for units greater than 15 miles from the city‐center change in relative terms by ?1.5% over the same period.     

The Performance of Commercial Mortgages

This study examines the return characteristics of a large, well-diversified commercial mortgage portfolio. Mortgage-specific cash-flow histories are constructed for 2,480 loans originated over the period 1974 through 1990, and a contingent-claims approach to pricing risky debt is used to estimate inter-temporal market values. Quarterly holding-period returns are compared across selected mortgage groups and to alternate asset classes. Our findings suggest that both mortgage returns and volatility of return are comparable to those of other forms of fixed-income assets over the study period. Implied property price volatility is found to average 17%, a result significantly higher than reported in earlier studies. While mortgage returns are found to vary by property type and region of origin, cross correlation of returns is found to be high, illustrating the systematic effect of interest rates on the performance of commercial mortgages over the period 1974 through 1990. However, an increase in credit risk in the latter years of the study suggests that diversification may be a worthwhile objective for holders of these assets. We do not find evidence to suggest that abnormal returns were earned on commercial mortgage portfolios over the study period.     

Returns and Information Transmission Dynamics in Public and Private Real Estate Markets

This paper examines U.S. public and private commercial real estate returns at the aggregate level and by the four major property types over the 1994–2012 time period. Returns are carefully adjusted for differences between public and private markets in financial leverage, property type focus and management fees. Unconditionally, we find that passive portfolios of unlevered core real estate investment trusts (REITs) outperformed their private market benchmark by 49 basis points (annualized) over the 1994–2012 sample period. Our baseline vector autoregression results suggest that REIT returns do not embed additional commercial real‐estate‐specific information useful in predicting private market returns. These results strongly suggest that equity REIT returns react to fundamental (latent) asset pricing information more quickly than private market returns given their greater liquidity and price revelation. REITs therefore serve as a fundamental information transmission channel to private market returns when asset pricing variables are omitted.     

Risk Segmentation of American Homes: Evidence from Denver

This article empirically examines the segmentation of house price risk across 99 ZIP‐code‐delineated neighborhoods in metropolitan Denver. The house price risk in each neighborhood is measured with the temporal variation of quarterly appreciation rates of the neighborhood house price index over the 2002–2007 period. Cross‐sectional regressions of neighborhood house price risk on the median household income and the percentage of population in poverty from the 2000 census data for the same neighborhoods provide strong evidence that the house price risk is significantly higher in low‐income/poor neighborhoods. Subperiod analyses further indicate that the risk segmentation exists in both a booming period (pre 2005:2) and a busting period (post 2005:3). The results indicate that homeownership can be a much riskier investment for low‐income/poor households.     

House Search Duration in Hot and Cold Residential Markets

This study analyzes the determinants of house search duration of consumption‐driven buyers and individual investors in different housing market environments. We use data from surveys of recent house‐buyers in “hot” and “cold” housing markets in the 2000s housing bubble in California characterized by rising and declining residential house prices, respectively. The average house price and the surveyed geographical area are the same for both periods. Expected house ownership horizon is shown to be an important determinant of the realized search duration in addition to commonly considered housing and buyer characteristics. We find a statistically significant positive effect of it on the time until purchase in both housing price environments for consumption‐driven buyers. We also find that consumption‐driven house purchases were highly pronounced in coastal areas in the hot market and inland areas in the cold market. In contrast, long‐horizon investment activity leads that of consumption activity in those areas. Short‐horizon investors, on the other hand, concentrated their house search activity in inland areas in both housing market environments.     

Market Efficiency and Return Statistics: Evidence from Real Estate and Stock Markets Using a Present-Value Approach

This paper develops a methodology to identify asset price response to news in the framework of the Campbell–Shiller log-linear present-value equation. We further show that a slow price adjustment in real estate markets not only induces a high serial autocorrelation in excess returns, but also dampens the return volatility and the correlation with excess returns in other asset markets. Using Hong Kong real estate and stock market data, we find that the quarterly real estate price assimilates only about half the effect of market news, whereas the quarterly stock price incorporates the news fully. Our analysis identifies a cumulative price adjustment that recovers lost information in real estate returns due to market inefficiency and thereby restores the real estate return volatility and the correlation between real estate and stock markets.     

Housing Price Volatility Changes and Their Effects

We examine significant volatility shifts in regional housing price changes, adapting a method of Haugen, Talmor and Torous (1991) independent of predefined sampling blocks. We identify 36 volatility events, most of which are purely regional, but three of which are national. We find significant associations of volatility events and economic conditions, especially national and regional income growth, inflation, and interest rates. During an initial adjustment period after a volatility shift, realized housing returns move opposite to volatility. We find evidence of significant interregional diffusion of volatility increases, but not of decreases. New insights on links between economic conditions and housing volatility and returns should be of value to household investors and mortgage investors.     

Cash to spend: IPO wealth and house prices

This study demonstrates the impact of initial public offerings (IPOs) on local house prices. Applying spatial difference-in-differences methods to IPOs in California from 1993 to 2017, we find house prices increase by 0.7%–0.9% near an IPO firm's headquarters around filing and issuing dates. Upon lockup expiration, price changes depend on postissuance returns. Treating the San Francisco Bay as a commuting barrier, we identify sustained price increases after filings and temporary increases after issuing and lockup expiration. We also confirm post-IPO price divergence between the treatment and synthetic control areas. Our findings indicate the effect of liquid wealth under mild financial constraints.     

Pay Me Now or Pay Me Later: Alternative Mortgage Products and the Mortgage Crisis

Mortgage contract design has been identified as a contributory factor in the recent market crisis. Here we examine alternative mortgage products (including interest‐only and other deferred amortization structures) and develop a game theoretic model of contract choice given uncertain future income and house prices across different types of borrowers. Results imply that deferred amortization contracts are more likely to be selected in housing markets with greater expected price appreciation and by households with greater risk tolerance; moreover, such products necessarily entail greater default risk, especially among lower‐income households who are aggressive in housing consumption levels. Empirical tests of model predictions generally provide support for the theory.     

REIT Stock Market Volatility and Expected Returns

We study the relation between REIT stock volatility and future returns, focusing particularly on the financial crisis period of 2007–2009. There is ongoing debate about whether stock volatility can forecast future returns. Our findings suggest that REIT‐implied volatility is negatively related to contemporaneous stock returns; there is a significant positive relationship between REIT implied volatility and future stock volatility; and there is a significant negative relation between REIT implied volatility and future stock returns. Lastly, we develop trading rules based on REIT implied volatility to test whether these relationships are exploitable. The result suggests a potentially profitable trading strategy.     

A Multivariate Commodity Analysis with Time-Dependent Volatility—Evidence from the German Energy Market

In recent years commodity markets (in particular electricity, coal, and emissions) encountered extreme price movements and phases of high price volatility. Utility companies are naturally exposed to these kinds of market movements and thus have to set-up an appropriate risk management system. We show that the nonstationary behavior of recent energy prices can be captured by time-dependent (and possibly stochastic) volatility models. We compare their statistical performance and their impact on risk management applications by calculating risk metrics such as value-at-risk. Based on a comprehensive backtesting study we conclude that our suggested models outperform stationary models in most cases and therefore should be considered superior for risk management applications.     

基于ESDA方法与空间计量模型的房价溢出效应分析

一个地区房价的波动可以通过时间滞后传递到其他地区，从而产生房价溢出效应。本文以我国31个省际区域2005～2014年的数据为样本，运用探索性空间数据分析（ESDA）对31个省的房价空间分布格局及演变态势进行分析。然后运用空间计量模型分析我国省际房价变动的影响因素，并从地理因素和经济因素两个方面研究房价溢出效应。研究结果表明：中国31个省域间的房价存在明显的空间相关性；地区的城市化率、城镇就业人员平均货币工资、二、三产业产值占GDP比重对房价有显著的影响，房价存在显著的空间溢出效应，经济距离对地区房价影响比地理距离的影响更显著。     

Do House Price Levels Anticipate Subsequent Price Changes within Metropolitan Areas?

This research examines the relationship between hedonically controlled housing price levels and subsequent changes in those prices across locations within MSAs. Are areas with a high price relative to an “imputed rent” paying for higher appreciation? In an efficient market (e.g., Gordon Growth Model), as fundamentals (impute rent) differ across locations and change over time, anticipation of these should generate a positive correlation between (residual) price levels and subsequent price changes. We undertake these tests in four different MSAs using a panel of repeat‐sale house price indices that have been scaled to price levels with the hedonic attributes of the house and ZIP code. In three markets we find that identical houses in higher priced ZIP codes subsequently appreciate faster. In one market we find that there is little statistical difference.     

A Model of Time‐on‐Market and Real Estate Price Under Sequential Search with Recall

This article develops a model and provides a closed‐form formula to uncover the theoretical relationship between real estate price and time on market (TOM). Our model shows a nonlinear positive price‐TOM relationship, and it identifies three economic factors that affect the impact of TOM on sale price. We demonstrate that conventional metrics for real estate return and risk, which are borrowed in a naïve fashion from finance theory, do not account for marketing period risk and tend to overestimate real estate returns and underestimate real estate risks. Our model provides a simple way to correct such bias. This theory helps to explain the apparent “risk‐premium puzzle” in real estate.     

The Adjustable Balance Mortgage: Reducing the Value of the Put

We propose a new mortgage contract that endogenously captures the risk of house price declines to minimize default risk resulting from changes in the underlying asset value while still retaining contract rates near the cost of a standard fixed‐rate mortgage. By reducing the role of the legal system in mitigating house price risk, the new mortgage reduces the negative externalities and social costs arising from defaults. In other words, the new mortgage minimizes the need to use the legal foreclosure system to deal with the economic risk of house price declines.     

Big Data Analysis of Volatility Spillovers of Brands across Social Media and Stock Markets

Volatility is an important metric of financial performance, indicating uncertainty or risk. So, predicting and managing volatility is of interest to both company managers and investors. This study investigates whether volatility in user-generated content (UGC) can spill over to volatility in stock returns and vice versa. Sources for user-generated content include tweets, blog posts, and Google searches. The authors test the presence of these spillover effects by a multivariate GARCH model. Further, the authors use multivariate regressions to reveal which type of company-related events increase volatility in user-generated content.Results for two studies in different markets show significant volatility spillovers between the growth rates of user-generated content and stock returns. Further, specific marketing events drive the volatility in user-generated content. In particular, new product launches significantly increase the volatility in the growth rates of user-generated content, which in turn can spill over to volatility in stock returns. Moreover, the spillover effects differ in sign depending on the valence of the user-generated content in Twitter. The authors discuss the managerial implications.