Characterizing Infill Residential Sites with Space-Time Clustering


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Building enough dense residential housing units near existing transit is essential to meeting California’s aggressive climate goals. Localities must understand where best to greenlight these dense developments so that sufficient construction actually takes place. Our project utilized space-time clustering and database management to analyze property price trends at the individual parcel level in La Mesa, California. To our knowledge, there have been no other spatio-temporal analyses like ours on the assessor’s parcel level. The project was conducted by students and faculty at San Diego State University in partnership with the City of La Mesa. We developed a program using Python and PostgreSQL to group properties into k space-time clusters, which demonstrate significant similarities in their price level over the period of study. From there, the appropriate parties could factor a property’s price trajectory into their assessment calculus. Land and improved price components of parcels were analyzed separately, as well as the combined price. We cleaned and constrained the available parcel data to yield the most accurate and relevant results possible. As an added externality, the clusters reveal undervalued properties beyond those under initial consideration. Our analysis was limited to the City of La Mesa due to time and computational resource constraints, but the program can be extended to a large county level. We hope our program will lead to better investments in smart residential growth in the City of La Mesa and will provide rich contextual information for future decision making.

Read the final student report delivered to the local gov/community partner.

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Kristofer Patron
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(619) 594-0103

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