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In the United States, homeownership is often considered the “American Dream” and has long been a central focus of housing policy. Homeowners benefit from various local, state and federal programs whose primary purpose is fostering homeownership. In 2014 alone, the federal government provided over $209 billion in homeownership subsidies (U.S. Department of the Treasury 2015).1 Justifications for such programs are derived from the belief that

homeownership creates positive externalities. While homeownership may create positive externalities questions regarding its impact on house prices remain. What is the monetary value of the positive externalities associated with homeownership? Does the monetary value of the positive externalities exceed their cost? Are the positive externalities allocated equally? Although these questions have important policy implications, difficulty in designing and

implementing a study that isolates homeownership’s effect on house prices has limited previous research on the topic.

This paper provides a framework to address these questions and offers new insight into the distributional effect of homeownership on house prices. It has two primary goals: (i) to isolate the effect of homeownership, and changes to homeownership rates, on nearby house prices and (ii) to examine the effect of homeownership across the full distribution of house prices. To the best of my knowledge, this is the first study to use parcel-level data to isolate the effect of homeownership, and changes to homeownership rates, on nearby house prices. Isolating the extent and nature of price differentials related to homeownership is difficult because a variety of other factors may be correlated with sales prices and homeownership levels. For example, house prices in neighborhoods with high homeownership likely vary in quality, both structurally and in terms of neighborhood amenities, compared to houses in neighborhoods with low

homeownership. Additionally, to properly isolate the effect of homeownership on house price, one needs to compare the effect homeownership has on prices for identical properties. However, due to the heterogeneous nature of real estate this is extremely difficult in practice.

I address these concerns and extend the extant literature using a unique dataset that provides information on sales prices, house characteristics, neighborhood quality, and buyer

1

To provide a sense of magnitude, the federal government’s subsidy for rental housing was $8.3 billion or just under 4% of the homeownership subsidy.

2

attributes. The dataset includes every single-family detached sales transaction that occurred in Fulton County, Georgia from 2002-2014. For each transaction I observe the sales price, location, and detailed housing characteristics. I then merge information about the race, sex, mortgage, and income of the buyer to the sales transactions. The dataset also includes annual panel data for the entire housing stock, allowing me to isolate homeownership rates while controlling for differing property type compositions and neighborhood amenities.

High-income households are the primary beneficiaries of the federal government’s

subsidization of homeownership.2 Despite this well documented fact, previous studies tend to

focus on the average causal effect of homeownership on house prices, so little is known about the relationship over the full distribution of house prices. Understanding the distributional effect of homeownership is important because promoting homeownership, particularly low and

moderate income as well as first time homeownership, has been a primary focus for

policymakers for several decades.3 Additionally, knowledge of the distributional effect of

homeownership provides a clearer picture of what is driving the mean results and provides insight into the allocation of the positive externalities associated with homeownership.

The results of this study have several important policy implications. First, using a

research design that explicitly controls for the unobserved quality of the individual house as well as time-varying neighborhood effects I find that the average causal effect of homeownership on house prices is much lower than previously reported. I estimate that a 10% increase in

homeownership results in a 2.6% increase in surrounding house prices.4 I also document the

existence of quantile effects. Ex ante, I would expect changes to homeownership rates to have a greater effect on house prices in the upper deciles of the conditional house price distribution, as the federal tax subsidies for homeownership combined with a progressive income tax favors high income households. However, I find that changes in homeownership rates have a lesser (greater) effect on house prices in the upper (lower) deciles of the conditional house price distribution.

2

Poterba and Sinai (2008) show that the mortgage interest tax deduction saves the average homeowner $1,060. However, the average savings for households who make more than $250,000 is $5,459, compared to $91 in savings for households whose annual income is less than $40,000.

3

For example, the Federal Housing Enterprises Financial Safety and Soundness Act of 1992 established performance standards for the Government Sponsored Enterprises to make homeownership available to a wider variety of households (Case et al., 2002).

4

Coulson and Li (2013) estimate that a 10% increase in homeownership increases house prices by approximately 6%.

3

The subsidization of homeownership should also, in theory, directly affect a household’s tenure choice. However, the lesser effect of homeownership on house prices in the higher deciles suggests that the federal tax subsidies for homeownership, which provide greater benefits to high-income households, are ineffective. Thus, if promoting homeownership is one of the primary goals of the federal tax subsidies, they are, at a minimum, poorly allocated.

The remainder of the paper is organized as follows. In the next section I provide a survey of the related literature. The paper then proceeds with a detailed overview of the dataset and homeownership measures used in this study. I then present the paper’s methodology and empirical results. The paper concludes with a discussion of the potential implications of the findings.