more stuff about real estate investing

office space are the “engines of growth” because entrepreneurial and creative activities are concentrated in office space. This suggests that office space grow by hosting new businesses and “churning” industries advantageously. In so doing, office space need to adapt their spatial structure to mitigate negative externalities. Our previous paper (Lee and Gordon 2007) found that the links between urban structure and growth vary across metro size: more clustering in small metros and more dispersion in large metros were associated with faster employment growth. In this paper, we extend our research to investigate to what extent urban spatial structure variables – dispersion and polycentricity – influence net new business formation (NNBF) and industrial “churning” in a cross-section of 79 U.S. metropolitan areas in the 2000s. The results of least squares regression and locally weighted regression analyses are mixed. OLS results for recent years fail to replicate out results for the 1990s. But applying a more powerful LOESS approach does give results for spatial impacts on NNBF and industrial churning that are consistent with the links between spatial structure and urban growth found in the earlier paper.

Revenue Code for Commerical Real Estate

Changes in the Internal Revenue Code create and remove tax-induced trading constraints on homeowners. The Taxpayer Relief Act of 1997 replaced a one-time, post- age 55 capital gain exclusion with a larger gain exclusion option that could be exercised every two years. We develop a simple demand-based specification of housing turnover and use it to determine whether the Taxpayer Relief Act of 1997 led to changes in the percentage of the existing housing stock that was sold in the U.S. and the four geographic regions defined by the U.S. Census Bureau (Northeast, Midwest, South and West). Commercial Real Estate is more viable for investment. We find that our macro measure of housing turnover increased significantly after the 1997 Act was passed. We augment this macro level analysis with household-level data to determine if these impacts were heterogeneous across age groups, across trading up and trading down, and across geography. The surprising result is how broad based the change in trading behavior is, appearing across all age ranges and impacting both trading up and trading down.