Mining Real Estate Data for Patterns Wolfgang Enterprises

1. Mining Real Estate Data for PatternsWolfgang Enterprises wolfson.matt@gmail.com 412 620 8359

2. MOTIVATION Real Estate acquisition and development is highly competitive. Decision to invest is often a combination of multiple information sources. Any tool that would give an advantage in recognizing an appreciating market before the fact becomes self evident has an advantage Data can also help city planning and determine investors vs home-buyers

3. Indicators • Most common indicators: • Price: Increases in price is one of the most watched indicators. • Location: Market values vary greatly based on location. • Physical characteristics of the property • Less Common indicators • Days on market: The less time a property spends on the open market is an good indicator of demand, even if prices have not increased. The ability to discern subtle trends in the number of days on market can lead to spotting a quickly appreciating market • Expired listings: Properties that did not sell and have been pulled off the market. • Cash sales: All cash transactions are a good indicator of investors moving into a market

4. Objectives • Develop software capable of pattern recognition in large data sets to reveal patterns that may not be readily evident • Requirements: • Identify sources of data, in addition to provided real estate data • Learn statistical correlations and packages that provide such info • Create any type of "data analytics” needed/desired. For example: • spotting trends that are not in the norm • prediction of where the market will go (hard) in a given local. • Identifying large turn-around hot spots or conversely identifying areas that may have peaked and no longer represent an investment opportunity