This project will build a theoretical model of real estate brokerage using assumptions based upon findings from the extensive brokerage literature. In this model differentiation in services and quality between brokerage firms combined with differentiation in preferences between sellers lead to measurable ranges of operation for brokerage firms. These ranges overlap, leading to the competitive nature of the industry. This theoretical model can be simulated in order to predict when ranges will grow or shrink and when competition within them will increase or decrease.

Using MLS data for Northern Colorado we will measure the range of operation in ArcGIS for each brokerage firm and each agent in the sample by using actual geocoded data for listings and transactions. These ranges of operation will be used to calculate a market share of listings or transactions for the agent or brokerage firm within their own range of operation. For example, while a county might have 1200 listings a certain brokerage firm within that county may compete only within a smaller area of that county in which there are 120 listings. If the brokerage firm has 40 total listings our methodology would give them a market share of 33% within their operating range rather than 3% within the county.

Market share measures for individual agents and brokerage firms will be combined to generate a market concentration measure for larger predefined areas such as cities or census blocks akin to the Herfindahl-Hirschman Index. This will be done using a weighted average, using conventional market share measures (the aforementioned 3%) for weights. Using panel data techniques we will test whether higher values for our market concentration measure are correlated with higher or lower sales prices. We will examine whether market shares and the size of operating ranges for individual agents and brokerage firms vary predictably with local market conditions. These tests will help to determine what value better measures of brokerage firm market share and market concentration will have to policy makers and real estate practitioners, potentially in identifying desirable locations for new entrants and in predicting future trends.