Location,location,location: House price returns and related topics in finance.

详细信息   

• 作者：DiVenti ; Theresa R.
• 学历：Doctor
• 年：2009
• 导师：Green, Richard K.,eadvisorKlock, Mark S.,eadvisorHwang, Minecommittee memberCalhoun, Charles A.ecommittee memberNeuhauser, Karyn L.ecommittee memberWilson, Arthur J.ecommittee member
• 毕业院校：The George Washington University
• Department：Finance
• ISBN：9781109211160
• CBH：3359583
• Country：USA
• 语种：English
• FileSize：2301717
• Pages：197

文摘

Every quarter since 1996, the Federal Housing Finance Agency formerly the Office of Federal Housing Enterprise Oversight) releases consistent house price indices for 379 metropolitan statistical areas MSAs). These data are one of the most cited sources for house appreciation. The availability of these data in the public domain provides reliable information on differences in the risk and return characteristics of local housing markets. This research consists of three essays that use the Federal Housing Finance Agency FHFA) data in new ways. I use these data to expand homeowners financial decision models related to portfolio theory and to develop new performance statistics for local housing markets. The first essay extends the mean-variance portfolio choice model for homeowners to include their house asset. I investigate whether the risk and return characteristics of a houses location play a key role in homeowners house purchase behaviors. For example, do homeowners in MSAs with high returns and low risks invest more in housing than their counterparts in areas with less favorable risk and return characteristics? I find that the risk and return characteristics of MSAs are important but are overshadowed by the regulatory constraints on land use. The main finding is that, in areas with high regulatory constraints, homeowners do the opposite of what the model predicts. Based on the risk and return characteristics of MSAs, homeowners purchase too much housing. In unconstrained areas, I find no relationship between the actual and predicted house purchase behaviors. In the second essay, I construct a cointegration model that estimates MSA-betas and an error-correction mechanism that estimates MSA speed-of-adjustment factors. These new performance statistics provide homeowners, investors, and policy makers with consistent measures to better assess local residential house price appreciation trends. In the current housing crisis, they help answer questions such as: Which MSAs are likely to see the largest or smallest housing price declines? Which MSAs will recover quickest? And which MSAs will take the longest to recover? The third essay considers another possible variable in the home price appreciation mix, the specialization and concentration of industries in a MSA. An intuitive assumption is that a MSA with concentrated industries, especially one-industry towns, would have a high risk of significant price movements. For example, a large factory would cause mass unemployment resulting in foreclosures and other downward price pressures. One would also assume the opposite； i.e., MSAs with diverse industrial bases and more stable human capital would have lower house price movement risk. The data leads to an interesting conclusion: While there is no correlation among industrial structure, human capital and house price risk, I find a relationship does exist when I account for industrial specialization. This research uses MSA-level house price indices to provide new location-based financial statistics and models. Equally important, it suggests the need for further research into the role of overlooked location-based variables in homebuyers purchase decisions such as land use regulations and industrial specialization and concentration. As the past decade made clear, a price momentum framework, where "getting into the housing market because everyone else is getting into it," is not a sound guide for buying a home. Location, and how diverse variables impact different areas, suggests a more reliable compass.