||The number of closed housing transactions is a direct indicator of local housing market conditions. Transaction volume is affected by many external factors. This study uses a unique database to examine local economic data from 27 markets in the US from 1990 to the third quarter of 2011 in order to examine the impact of changing macroeconomic conditions on single-family housing transaction volume, an indicator of market conditions for a specific area. Our model produces an elasticity or sensitivity coefficient that measures how much transaction volume changes for a given change in national economic conditions, as measured by the year over year percentage change in the real US Gross Domestic Product (GDP). We use summary metropolitan-level data on single family homes that was generously provided by the National Association of Realtors and Capital Analytics. Local measures of housing market conditions include the number of regular (nondistressed) sales recorded taken from metropolitan and county-level tax assessor records. Other variables included in the analysis include average selling price per square foot, time-on-the-market, change in employment, foreclosures as a percentage of sales, and the differential between the 3-month LIBOR and the 3-month US Treasury rate to proxy for international risk. It is envisioned the real estate market participants could use the model to examine how the number of market transactions in each of these markets have responded to a change in the GDP. This model will also allow us to compare housing markets and we hope to eventually extend the analysis to forecast future market conditions. In addition, we will be able to examine how transaction levels have shifted over time. This proposed model and its output will be useful in raising the understanding of how a housing market will respond to changing stimuli, which in turn will be useful to real estate brokers in planning future activities, to lenders in deciding where to allocate their capital and to public planners making fiscal policy.