If you’re like most home sellers and buyers – you want an edge over your competition. What better way to get the edge than having a way to predict the market. If you don’t have a working crystal ball, there are a few methods to forecast and measure housing (some of which have been used in empirical research).
by Dan Krell © 2013
Various studies demonstrate that you can assess and somewhat predict activity in a housing market; which, albeit in hindsight, can assist home sellers and buyers in determining whether it is a good time to sell or buy a home. For example, I recently wrote about gauging real estate through divorce and premarital agreements; which discussed the implications of these life events to the housing market. The increase in prenups could indicate an increased perception in the value of home ownership and possibly the overall housing market.
Another recent study indicated that it may be possible to determine home pricing through internet search data. Beracha and Wintoki (Forecasting Residential Real Estate Price Changes from Online Search Activity; The Journal of Real Estate Research 35.3 (2013): 283-312.) set out to find out if keyword search engine data from Google could determine price shifts in various cities. They concluded that this may be the first study that directly links “aggregated” search engine data to “abnormal crosssectional home price changes.”
Essentially, the research established that you can figure out metro housing market activity through Google Trends and Google Insights, which provide keyword volume measurement in internet searches. The study examined the keywords “real estate [city]” from 2004 through 2011, and concluded that “…cities associated with abnormally high real estate search intensity consistently outperform cities with abnormally low real estate search volume by as much as 8.5% over a two-year period.”
And although the study’s authors discussed prior research linking internet keyword searches and consumer behavior, they caution that there are a number of keywords related to real estate that may be more relevant than the keywords used in their study. Regardless, the authors assume that their results may be useful in home sales and purchases. More importantly, it may seem as if their results may strengthen the link between specific search engine keywords (e.g, “real estate Rockville” or “real estate Bethesda”) and the ability to predict a housing bubble, or possibly home price peaks.
Generalized, “global” data, such as those described in Beracha and Wintoki’s study, and their meaning may be interesting; however, limiting yourself to such indiscriminate analysis for your home sale or purchase could be disadvantageous. Global data does not distinguish the many factors that impact regional markets; nor can it sort out differences within a local market (neighborhood data within a region can vary significantly).
Using “global” tools may be useful; however, if you are planning a home sale or purchase – seek out the assistance of a local Realtor®. Your real estate agent has access to local specific data that is reported through the MLS. Using MLS data, your agent can prepare a market analysis that compares your home to recent neighborhood sales; the breakdown can put your home in perspective and can give you a price range to assist you in listing or purchasing the home. Additionally, your agent can provide a hyper-local trend analysis so as to help you understand what to expect from the local housing market.
More news and articles on “the Blog”
Disclaimer. This article is not intended to provide nor should it be relied upon for legal and financial advice. Readers should not rely solely on the information contained herein, as it does not purport to be comprehensive or render specific advice. Readers should consult with an attorney regarding local real estate laws and customs as they vary by state and jurisdiction. This article was originally published the week of November 11, 2013 (Montgomery County Sentinel). Using this article without permission is a violation of copyright laws. Copyright © 2013 Dan Krell.