House Price Changes and Idiosyncratic Risk: The Impact of Property Characteristics

Bourassa, Steven C.; Haurin, Donald R.; Haurin, Jessica L.; Hoesli, Martin; Sun, Jian

doi:10.2139/ssrn.873451

Cited by 21 publications

(26 citation statements)

References 46 publications

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“…Stated differently, a high land leverage indicates that a significant amount of the rental income of the investor is exposed to the attractiveness of one particular location. An important source of volatility in prices (and rents) is the evolution of the attractiveness of land (Bostic et al, 2007;Davis & Heathcote, 2007;Bourassa et al, 2009Bourassa et al, , 2011Nichols et al, 2013). As the investor's exposure to the location risk factor is high whenever the land leverage is high, a higher risk premium is expected.…”

Section: Proxies For Micro-level Risk Premiamentioning

confidence: 99%

Transaction-Based and Appraisal-Based Capitalization Rate Determinants

Chaney¹,

Hoesli

2012

SSRN Journal

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This paper contributes to the debate about capitalization rate determinants by comparing the driving factors of appraisal-based cap rates with those of transaction-based cap rates. By using a rich database of real estate transactions in Switzerland for the period of 1985-2010, we identify several property-specific variables that have not been used in prior research and that increase the explained portion of the cap rate variance by as much as 10 percentage points. The results show that compared to investors, appraisers overweight factors that they can easily observe when they appraise a property, at the cost of variables related to growth expectations and the opportunity cost of capital. This has two implications. First, as the easily observable factors hardly change over time, while the latter variables change frequently and significantly, it provides new evidence that may add to the appraisal-smoothing discussion.Second, investors put less emphasis on factors that are diversifiable, which suggests that they favor a portfolio perspective, whereas the focus of the appraisers is more on the individual property level.

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Section: Proxies For Micro-level Risk Premiamentioning

confidence: 99%

Transaction-Based and Appraisal-Based Capitalization Rate Determinants

Chaney¹,

Hoesli

2012

SSRN Journal

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“…Goetzmann (1993) also documents a substantially higher variation for individual properties, with standard deviations 1.5 to 3 times higher for four US metropolitan areas. For New Zealand, Bourassa et al (2005) find standard deviations 1.4 to 2 times higher than the ones of the general market. They relate the degree of variation in price changes among houses within a market to their characteristics and to the prevailing conditions of the housing market at the time of the sale.…”

Section: Is An Index-hedge Appropriate?mentioning

confidence: 71%

Property Derivatives and Index-Linked Mortgages

2006

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Economists have forcefully argued for the introduction and use of property derivatives as a hedge against house price risk (e.g. Shiller and Weiss, 1999). The rationale for these financial instruments seems clear, as many households are heavily invested in housing and standard financial instruments offer a poor hedge. In practice, however, most of the property derivatives available have been targeted to meet the needs of institutional investors, not those of owner-occupiers. Building on the recent launch of the first Swiss property derivative, we here propose index-linked mortgages tailored to retail consumers. The payments of these mortgages depend on the corresponding housing market performance. We further discuss the stabilization of the homeowner's net wealth, price the instruments, and quantify the expected decrease in the mortgage default risk achieved by these immunization effects.

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“…Secondly, the use of a house price index and aggregate income underestimates the volatilities of an individual house price and the labor income of a typical worker. We increase the volatility of house prices from the estimated value of 6.1% to 12%, which is identical to the value assumed by Flavin and Yamashita (2002) and Yao and Zhang (2005) and in the range estimated by Case and Shiller (1989) and Bourassa, Haurin, Haurin, Hoesli, and Sun (2009). Furthermore, we increase the income volatility from the estimate of 2.1% to 10%, in line with the estimate in Cocco, Gomes, and Maenhout (2005).…”

Section: Calibrationmentioning

confidence: 80%

Predictors and Portfolios Over the Life Cycle

2016

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We show that the net corporate payout yield predicts both the stock market index and house prices and that the log home rent-price ratio predicts both house prices and labor income growth. We incorporate the predictability in a rich life-cycle model of household decisions involving consumption of both perishable goods and housing services, stochastic and unspanned labor income, stochastic house prices, home renting and owning, stock investments, and portfolio constraints. We find that households can significantly improve their welfare by optimally conditioning decisions on the predictors. For a modestly risk-averse agent with a 35-year working period and a 15-year retirement period, the present value of the higher average life-time consumption amounts to roughly $179,000 (assuming both an initial wealth and an initial annual income of $20,000), and the certainty equivalent gain is around 5.5% of total wealth (financial wealth plus human capital). Furthermore, every cohort of agents in our model would have benefited from applying predictor-conditional strategies along the realized time series over our 1960-2010 data period.Keywords: Return predictability, human capital, housing, investments, welfare JEL subject codes: G11, D91, D14 Predictors and Portfolios over the Life CycleAbstract: We show that the net corporate payout yield predicts both the stock market index and house prices and that the log home rent-price ratio predicts both house prices and labor income growth. We incorporate the predictability in a rich life-cycle model of household decisions involving consumption of both perishable goods and housing services, stochastic and unspanned labor income, stochastic house prices, home renting and owning, stock investments, and portfolio constraints. We find that households can significantly improve their welfare by optimally conditioning decisions on the predictors. For a modestly risk-averse agent with a 35-year working period and a 15-year retirement period, the present value of the higher average life-time consumption amounts to roughly $179,000 (assuming both an initial wealth and an initial annual income of $20,000), and the certainty equivalent gain is around 5.5% of total wealth (financial wealth plus human capital). Furthermore, every cohort of agents in our model would have benefited from applying predictor-conditional strategies along the realized time series over our 1960-2010 data period.

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House Price Changes and Idiosyncratic Risk: The Impact of Property Characteristics

Cited by 21 publications

References 46 publications

Transaction-Based and Appraisal-Based Capitalization Rate Determinants

Transaction-Based and Appraisal-Based Capitalization Rate Determinants

Property Derivatives and Index-Linked Mortgages

Predictors and Portfolios Over the Life Cycle

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