The Age–Period–Cohort Problem in Hedonic House Prices Models

The age–period–cohort problem has been studied for decades but without resolution. There have been many suggested solutions to make the three effects estimable, but these solutions mostly exploit non-linear specifications. Yet, these approaches may suffer from misspecification or omitted variable bias. This paper is a practical-oriented study with an aim to empirically disentangle age–period–cohort effects by providing external information on the actual depreciation of housing structure rather than taking age as a proxy. It is based on appraisals of the improvement values of properties in New Zealand to estimate the age-depreciation effect. This research method provides a novel means of solving the identification problem of the age, period, and cohort trilemma. Based on about half a million housing transactions from 1990 to 2019 in the Auckland Region of New Zealand, the results show that traditional hedonic prices models using age and time dummy variables can result, ceteris paribus, in unreasonable positive depreciation rates. The use of the improvement values model can help improve the accuracy of home value assessment and reduce estimation biases. This method also has important practical implications for property valuations.

Do Restrictive Land Use Regulations Make Housing More Expensive Everywhere?

This study makes three contributions to the debate over the effect of local land use regulations on housing prices and affordability. First, it is more geographically extensive than previous studies, encompassing 336 of the nation's 384 metropolitan areas. Second, it looks at multiple measures of regulatory stringency, not just one. Most prior studies have focused either on a single regulatory measure or index across multiple metropolitan areas, or multiple regulatory measures in a single region. Third, this paper considers the connection between regulatory stringency and housing values as a function of employment growth and per-worker payroll levels. We find that restrictive land use regulations do indeed have a pervasive effect on local home values and rents, and that these effects are magnified in faster-growing and more prosperous economies. We also find more restrictive land use regulations are not associated with faster rates of recent home value or rent growth, and that their effects on housing construction levels—that is, the degree to which they constrain supply—is uneven among different housing markets.

The impact of COVID-19 on home value in major Texas cities

Purpose This study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects. Design/methodology/approach This study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic. Findings The regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature. Originality/value Previous literature analyzed the housing market within the first phase of COVID-19, whereas this study analyzed the effects of the COVID-19 throughout the entirety of 2020. The mixed model includes spatial and temporal analyses as well as provides insight into how quantitative-based mobility behavior impacted housing price, rather than relying on qualitative indicators such as shutdown order implementation.

Machine Learning about Treatment Effect Heterogeneity: The Case of Household Energy Use

We use causal forests to evaluate the heterogeneous treatment effects (TEs) of repeated behavioral nudges toward household energy conservation. The average response to treatment is a monthly electricity reduction of 9 kilowatt-hours (kWh), but the full distribution of responses ranges from -40 to +10 kWh. Households learn to reduce more over time, conditional on having responded in year one. Pre-treatment consumption and home value are the most commonly used predictors in the forest. The results suggest the ability to use machine learning techniques for improved targeting and tailoring of treatment.

Appraising Value

This chapter analyzes how appraisers assess home value. It demonstrates that despite surface changes to appraisal requirements, the logic and methods guiding contemporary appraisers’ work reflected the explicitly racist appraisal logic and methods instituted by the U.S. federal government and the appraisal industry in the early and mid-twentieth century. When using such logic, appraisers assumed that racially uniform, White neighborhoods were the most valuable. They also assumed that White home buyers were the reference point for neighborhood desirability and value. This logic guided their methods, such that they typically chose “comps” from within singular neighborhoods. This chapter also uses quantitative data to show that homes in White Houston neighborhoods were systematically appraised higher than homes in otherwise similar Black and Latinx Houston neighborhoods in 2015. Such inequality is not merely an artifact of explicitly racist historical appraisals; rather, it is actively produced by contemporary appraisers.

The Long-Term Care Financing Challenge: What Role for Home Equity?

With increasing longevity comes the need for support in activities of daily living. This growing demand for long-term services and supports (LTSS) threatens older adult financial security. About 25 million adults aged 65 and older will have a significant LTSS need that will cost up to $140,000 out-of-pocket plus an estimated $120,000 from public payers like Medicaid; the total public and private costs of care exceeding $250,000. Home equity, the most valuable financial asset owned by Americans, offers one solution to this financing challenge. While seniors boast $7 trillion in collective home equity, opportunities to annuitize this asset vary and the distributions of home ownership and value are highly skewed. Using the 2016 wave of the HRS, this paper explores the relationship between socio-demographic/health characteristics and the annuitized home value. We determine which individuals with LTSS needs might benefit from the annuitization of home equity and to what extent.