Housing and Spatial Issues in Economics

Paper Session

Saturday, Jan. 7, 2017 10:15 AM – 12:15 PM

Sheraton Grand Chicago, Grant Park
Hosted By: Society of Government Economists
  • Chair: Farhad Niami, DC Office of the Chief Financial Officer

Investment Externalities in the Housing Market: Evidence from Historical Rehabilitative Tax Credits

Scott Wentland
,
U.S. Bureau of Economic Analysis
Geoffrey Turnbull
,
University of Central Florida
Bennie Waller
,
Longwood University
Walter Witschey
,
Longwood University
Velma Zahirovic-Herbert
,
University of Georgia

Abstract

Previous research has found that local amenities and neighborhood attributes can substantially impact the surrounding housing market. The external impact is often a key Pigouvian rational for federal and state tax policy subsidizing investment in rehabilitating historical properties via rehabilitative tax credits (RTC). This study examines the empirical question of whether property investment or rehabilitation has spillover effects on nearby properties utilizing a unique data set of properties salvaged through the use of RTCs in Virginia. Using over a decade of data from a regional MLS, the results indicate that homes in close proximity to RTC properties sell for a premium, but only limited evidence of small liquidity effects. We find a strong, pronounced price externality effect for homes outside historic districts, and incidentally, little (if any) externality from additional RTCs within historic districts.

Driving Past Commuting Zones: Re-Examining Local Labor Market Definitions

Andrew Foote
,
U.S. Census Bureau
Mark Kutzbach
,
U.S. Census Bureau
Lars Vilhuber
,
Cornell University and U.S. Census Bureau

Abstract

This paper evaluates the suitability of defining a local labor market based on commuting patterns for a variety of applications, not all of which are commuting-related. In particular, we revisit the Commuting Zone definitions (Tolbert and Sizer, 1996), and extend the concept to other criteria of connectedness of counties. We also show that the underlying uncertainty in commuting flows is not reflected in the typical analysis that uses the pre-computed commuting zones, and show how this affects empirical estimates using commuting zones. We provide guidance to remedy these issues. Finally, we propose a data-driven method to select the optimal set of local labor markets, using a variety of metrics, including commuting patterns, wages, and labor market flows. We compare our method to other candidate methods in the literature, and show that it is competitive with these definitions.

Where the Wealth Is: The Geographic Distribution of Wealth in the United States

Rebecca Chenevert
,
U.S. Census Bureau
Alfred Gottschalck
,
U.S. Census Bureau
Mark A. Klee
,
U.S. Census Bureau
Xingyou Zhang
,
U.S. Census Bureau

Abstract

Household net worth, or wealth, is known to exhibit a highly skewed distribution. Estimates of wealth concentration show that the top 0.1 percent of families held 22 percent of the wealth owned by U.S. households in 2012 (Saez and Zucman, 2014). However, household wealth is a difficult concept to measure. In order to create estimates of net worth for different areas and groups that are comparable across the country, we need a data source that is comparable across the nation and has enough data to measure the many components of net worth.

New research being undertaken at the Census Bureau combines information from the Survey of Income and Program Participation (SIPP), and the American Community Survey (ACS) to create wealth estimates for smaller geographies and smaller populations than were previously available. The SIPP is nationally representative and has rich and detailed information on wealth, while the ACS has more limited information on wealth but has a very rich sample with a diversity of geographic areas represented. This paper presents preliminary estimates of wealth and inequality at sub-national levels from the ACS, and seeks to validate and improve the estimation.
Discussant(s)
Ray Brastow
,
Federal Reserve Bank of Richmond
Kyle Hood
,
U.S. Bureau of Economic Analysis
Wenhua Di
,
Federal Reserve Bank of Dallas
JEL Classifications
  • H0 - General
  • R0 - General