Average Home Price Research Articles

Shedding light on large-scale solar impacts: An analysis of property values and proximity to photovoltaics across six U.S. states

We examine the impact of large-scale photovoltaic projects (LSPVPs) on residential home prices in six U.S. states that account for over 50% of the installed MW capacity of large-scale solar in the U.S. Our analysis of over 1,500 LSPVPs and over 1.8 million home transactions answers two questions: (1) what effect do LSPVPs have on home prices and (2) does the effect of LSPVP on home prices differ based on the prior land use on which LSPVPs are located, LSPVP size, or a home's urbanicity? We find that homes within 0.5 mi of a LSPVP experience an average home price reduction of 1.5% compared to homes 2–4 mi away; statistically significant effects are not measurable over 1 mi from a LSPVP. These effects are only measurable in certain states, for LSPVPs constructed on agricultural land, for larger LSPVPs, and for rural homes. Our results have two implications for policymakers: (1) measures that ameliorate possible negative impacts of LSPVP development, including compensation for neighbors, vegetative shading, and land use co-location are relevant especially to rural, large, or agricultural LSPVPs, and (2) place- and project-specific assessments of LSPVP development and policy practices are needed to understand the heterogeneous impacts of LSPVPs.

Fracking and Tracking: The Effects of Oil and Natural Gas Well Locations on the Housing Market.

The technique of hydraulic fracturing or fracking, has made it possible to produce vast new quantities of oil and natural gas. States like Colorado, Texas, and Oklahoma have seen a dramatic increase in the number of wells for both oil and natural gas. In this study, the main source of exogenous variation to be explored is the location of injection well sites. With that I estimate the effect hydraulically fractured natural gas and oil injection sites have on both urban and rural residential home prices between years 2000 to 2018 in 11 different states. The data is coming from two rich data sets, one form the U.S. Department of Homeland Security that listed all oil and natural gas wells. The second comes from Zillow's ZTRAX data base and that contains home transaction and administrative data. ArcGis is used to combine the data and created varying buffer zones sizes around injection well sites to see how average home prices and the average number of transactions changed before and after a well is put in, and as distance from the well site increases. First, a zip code level fixed effects model, as well as a household level fixed effects model to see how only repeat sales are affected by new wells. Lastly, a spatial differences in differences (SDID) is used to see how homes prices change as homes that are already by wells see a new well come in that decreases the distance to the nearest well. My results show that homes that are within .5 mile of a well have a 2.9% increase in selling price and homes that are .5-1 mile from a well site see a 1.2% increase compared to homes that are more than 2 miles away. Lastly, the spatial model shows that home that initially start 1-2 miles from a well and a new well is drilled reducing the distance to .5-1 miles away see a 2.1% increase and homes that are now .5 miles or closer see a 5.1% increase.

Determinants of housing prices: evidence from Ontario cities, 2001-2011

Determinants of housing prices: evidence from Ontario cities, 2001-2011

Do Higher Housing Values Make Communities More Conservative? Evidence from the Introduction of E-ZPass

This study uses the introduction of E-ZPass in Pennsylvania and New Jersey in order to identify the effect of changing property values on support for US political parties. We show that E-ZPass caused a decline in support for Democratic Presidential candidates. We argue that this effect is not spurious, but rather due to the fact that precincts near E-ZPass toll locations experienced a downward shock to transportation cost and a corresponding rise in average home price. We rule out other explanations dealing with turnout, community change, or rising incomes, and replicate the design in Ohio. By conducting a sensitivity analysis, placebo test, and using multiple dependent variables, we demonstrate robustness.

Metropolitan area home prices and the mortgage interest deduction: Estimates and simulations from policy change

We simulate changes to metropolitan area home prices from reforming the Mortgage Interest Deduction (MID). Price simulations are based on an extended user cost model that incorporates two dimensions of behavioral change in home buyers: sensitivity of borrowing and the propensity to use tax deductions. We simulate prices with both inelastic and elastic supply. Our results show a wide range of price effects across metropolitan areas and prospective policies. Considering behavioral change and no supply elasticity, eliminating the MID results in average home price declines as steep as 13.5% in Washington, D.C., and as small as 3.5% in Miami-Fort Lauderdale, FL. Converting the MID to a 15% refundable credit reduces prices by as much as 1.4% in San Jose, CA, San Francisco, CA, and Washington, D.C. and increases average price in other metropolitan areas by as much as 12.1% (Miami-Fort Lauderdale). Accounting for market elasticities produces price estimates that are on average 36% as large as standard estimates.

Metropolitan Area Home Prices and the Mortgage Interest Deduction: Estimates and Simulations from Policy Change

We simulate changes to metropolitan area home prices from reforming the Mortgage Interest Deduction (MID). Price simulations are based on an extended user cost model that incorporates two dimensions of behavioral change in home buyers: sensitivity of borrowing and the propensity to use tax deductions. We simulate prices with both inelastic and elastic supply. Our results show a wide range of price effects across metropolitan areas and prospective policies. Considering behavioral change and no supply elasticity, eliminating the MID results in average home price declines as steep as 13.5 percent in Washington, D.C., and as small as 3.5 percent in Miami-Fort Lauderdale, Florida. Converting the MID to a 15 percent refundable credit reduces prices by as much as 1.4 percent in San Jose, California, San Francisco, California, and Washington, D.C., and increases average prices in other metropolitan areas by as much as 12.1 percent (Miami- Fort Lauderdale). Accounting for market elasticities produces price estimates that are on average thirty-six percent as large as standard estimates.

Property taxes and home prices: A tale of two cities

We explore the influence of property taxes on home prices, taking advantage of a policy experiment of property taxation in Shanghai and in Chongqing starting from January 2011. Using the approach suggested by Hsiao, Ching and Wan (2012) we estimate hypothetical home prices in the absence of property taxation for Shanghai and Chongqing using home prices in other cities and provinces. We show that the OLS generates consistent estimators when the price series are non-stationary I(1) processes. We apply the model to a panel of average home prices of 31 cities and provinces in China, and find the property-tax experiment lowered the Shanghai average home price by 11%–15% but raised the Chongqing average home prices by 10%–12%. An examination of the policy details and data on prices by home types suggests the post-treatment price increase in Chongqing can be driven by a spillover effect from high-end to low-end properties.

Hot Money Flow, Money Supply, Mortgage Credit and Residential Property Prices in China

This paper assesses the linkages between economic activity, foreign reserve, money, credit, inflation and house prices in China over last decade. The analysis is based on a structural VAR estimated using monthly data spanning the period June 2001-December 2011. We find important responses of house price to economic activity, monetary policy and credit shocks as well as to foreign reserve shocks for overall average residential property price index and province indexes and the response patterns are not uniform among indexes. In particular, in Beijing, foreign reserve shocks had much stronger impacts on average residential property price during the late 2004 to the early 2005 but its contribution shrunk largely and sometimes turned to negative after the Lehman Shock. Again, Beijing’s average home price was boosted by the expansionary monetary policy and the loosing credit policy during March 2009 to June 2010. Nonetheless, the variances of a residential property price index are predominantly due to its own shocks.

Efficiency in the California Real Estate Labor Market

Problem statement: This research explores the extent of market efficiency in the real estate labor market. Given a common commission rate, areas with high average home prices will generate higher agent income per home sold. If markets are efficient with few barriers to entry, additional agents per capita would be expected in high-priced areas, but each home sale would represent a larger portion of an agent’s annual income so a risk premium should be present. Approach: Agent earnings and the number of homes sold were examined in selected California counties. The data provides details on over 200,000 transactions, for nearly 47,000 different real estate agents and brokers, with usable data for 477 distinct zip codes. Results: Results show that regions with a higher median home price have a greater number of parttime real estate agents and an increased number of agents per capita. Conclusion: There are fewer average commission events per agent in areas with higher housing prices, but a higher level of total commission earnings per agent to compensate for the added income risk per completed transaction.

End in Sight for Housing Troubles?

A historical relationship between home prices and family income is examined based on more than 40 years of data. A new home affordability ratio based on the average home price, family income and mortgage rates is analyzed in the historical context. This indicator is used to gauge the current state of the residential housing market in the United States. Historical data points to an imminent but slow recovery in the housing market over the next few years.