Real estate data consists of property, lender, lessee, and even climatic data. Whatever impacts property value or building quality makes up the larger category of data.
This data comes from many sources. First are official property records like square footage, municipal zones, and permits for updates and additions.
Other sources include neighborhood demographics, school ratings, local climate and weather, and geospatial data. Naturally, geospatial data is especially important to this category. Property location and relation to neighboring points of interest directly drives foot traffic and buyer interest.
Finally, there are online sources like review websites, forums, and blog posts.
While they often provide maps, data analysts typically create datasets in spreadsheet form that can be easily converted to graphs.
Attributes of the data vary by the real estate aspect you are researching but they invariably include location, usually down to the precise address of a property or cardinal location of windows.
Data analysts also commonly include time periods. These may be years, quarters, or some other period in relation to an event. For example, you may have columns listed as “median household sale price pre-economic recession of 2008.”
A range of people—individuals and corporations—use this data for a number of reasons. Individuals use it to find properties to buy or rent while real estate agencies use it to advertise to buyers and renters.
Real estate agencies and developers use it to determine if they should upgrade properties to keep up with neighborhood trends.
Investors and insurers use this data to appraise properties and predict trends. Lenders use the data to predict whether a loan applicant can reasonably pay back a loan or mortgage.
Finally, government planners use this data to assess local market health and determine whether to make changes to city infrastructure or to add features like parks or schools to a neighborhood.
There is an array of possible sources of real estate data that you can enrich your dataset with, so it can be difficult to know what or how much to include. However, if you always keep your goal in mind, you can begin crafting a complete and insightful dataset.
Beyond this, test real estate data in the same way as any other: make sure the sources are accurate and complete and that the dataset you compile with them is frequently updated and kept clean.
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