Affirmatively Furthering Fair Housing (AFFH) Making Better Decisions 🔎
What did we do?
Using its Hybrid Intelligence Predictive Modelling Platform (HIPRE) Presage Research created Bayesian machine learning P(C|X) prediction models for:
1.- C=Unaffordable housing (top 10% of census tracts in GCCOG with most unaffordable housing as defined by rent > 30% income)
2.- C=Evictions (census tracts in GCCOG with evictions using HRC data)
3.- C=Unsheltered homelessness (top 10% of census tracts in GCCOG with highest number of unsheltered homeless using LAHSA data)
In order to predict:
1) where there is higher/lower incidence of these problems;
2) where there may be higher/lower incidence of these problems in the future;
3) why there is a higher/lower incidence;
4) what interventions may be anticipated to be succesful in reducing the incidence
300+ Demographic, Economic, Social, Housing, Zoning and Transportation factors were incorporated from multiple sources: ACS, LAHSA, HRC, AFFH 2.0.
#Presage #AFFH #HybridIntelligence #Prediction #results