Saran Ahluwalia

Saran Ahluwalia

My primary interests are in applying statistical learning, graphical models and unsupervised machine learning in service of - amongst others - healthcare services, quantitative finance and real estate economics. I hope to continue to marry my prior experience with my passion for the democratization of knowledge through open source development

Predicting Gentrification in District of Columbia Wards

Date:

I was part of a interdisciplinary group - aptly named Gentrifuge - tasked with creating a solution during a three month sprint with the Department of Commerce and Small Business Administration. The problem was framed as such:

Research Questions

  1. Can we make a prediction on if a Starbucks will be available in the future in DC (or any other large city)?

  2. Can we define the probability of this defined by user preferences?

  3. Does a Starbucks indicate certain income threshold or budget breakdown for different locations (zip code, neighborhood) depending on the persons gross household income and/or household size?

  4. Can we recommend or predict where the next Starbucks will be available and how far it will be from the individual based on their life preferences and other information/ preferences?

  5. What are the strongest dependant variables (ie. costs for housing, grocery or insurance; or perhaps walkability score?) that feed into the availablility of a Starbucks (as an indicator of gentrification)?

  6. Can we predict if ‘making it’ will change over time due to inflation or other changes in consumer prices?

Hypothesis

  1. Utilizing open data sources (open and user-given) and by using widely accepted models for probability it can be inferred the percent chance that a Starbucks will be available within a certain time frame

Value Proposition

  1. Provide small businesses and enterprises information on the transitions of certain regional areas within cities that may indicate a growing market need for both goods and services

  2. Provide individuals with an indicator that may inform their choice to relocate, invest in a property or invest their leisure time in a specific location

  3. Provide non-profits and government agencies with information on the changing demographics of cities in order to invest human capital, monetary investments and social services (for those who may be displaced)

Result

We provided a dashboard which provided daily updates - by D.C. ward - on changing patterns in consumer growth and revenue generation.

We also provided a scoring metric for demand for affordable housing due to gentrification. This was provided at the granularity of street and block within a neighborhood (i.e. P street and 32nd street).

Suggested citation:

Ahluwalia, S, Clare, D, Caputo, T., Chen, J. & Grandison, T. (2015, October). Predicting Gentrification in District of Columbia Wards. Presentation at the annual District Data Labs Fellowship Symposium, Washington, DC.