A team from NYU examines the relationship and discovers an interesting result.
New York University’s Furman Center for Real Estate and Public Policy recently published the 2008 edition of State of New York City’s Housing and Neighborhoods . The study concludes that past performances of a neighborhood will not necessarily predict how much its housing values will fall. As stated in the 156-page report, “Predicting which neighborhoods will do well or will fare poorly is very difficult. There is less correlation between how a neighborhood does in one cycle and how it does in the next than one might expect. However, a few general principles stand out (like) price trends during past downturns are not reliable predictors of price trends in future downturns. Whether a neighborhood fared poorly or well in the 1974–1980 housing bust had little relationship to how that neighborhood performed in the next downturn.”
But everything’s not that hard to search for a positive effect. The report also found out that city investment in renovation, rehabilitation and new construction of housing helped stabilize home prices during the 1990’s downturn. It states, “City investment is correlated with greater stability in poor neighborhoods. Poor neighborhoods that received significant public investment to rehabilitate and increase their affordable housing stock experienced smaller price declines in the 1990s downturn, and in some cases even saw prices increase during that period. Indeed, City investment was more closely related to smaller housing price declines than any other neighborhood characteristic we studied.”
So what do these results imply?
If the study can only be expanded nationwide (albeit it will cost a lot and take longer to produce), we can actually find better predictions that are not only limited in New York City. If the findings remain the same, then we can say that today’s crisis will only speak little of what the future in home prices will be.
Second, this should hopefully encourage analysts to come up with a more accurate methodology that can reduce statistical errors in predicting housing values.
Finally, the second conclusion should influence more state officials to put their funds in housing programs especially those experiencing very high foreclosure rates like California, Florida and Nevada.