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Activity Number: 464 - JASA A&CS Special Invited Session
Type: Invited
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: JASA Applications and Case Studies
Abstract #320568
Title: Measuring Housing Vitality from Multi-Source Big Data and Machine Learning
Author(s): Jianqing Fan* and Yang Zhou and Lirong Xue and Zhengyu Shi and Libo Wu
Companies: Princeton University and Fudan University and Princeton University and Fudan University and Fudan University
Keywords: socioeconomics; machine learning; Housing Activeness; FarmPredict; Heterogeneity; Image data
Abstract:

Measuring timely high-resolution socioeconomic outcomes is critical for policy making and evaluation, but hard to reliably obtain. With the help of machine learning and cheaply available data such as social media and nightlight, it is now possible to predict such indices in fine granularity. We present an adaptive way to measure the time trend and spatial distribution of housing activeness with the help of multiple easily accessible datasets. We introduce the factor-adjusted regularization methods for prediction (FarmPredict) to deal with dependence and collinearity issues among predictors by effectively lifting the prediction space. The heterogeneity of big data is mitigated through the land-use data. FarmPredict allows us to extend the regional results to the city level, with a 75% out-of-sample explanation of the spatial and timeliness variation in the house usage. Since energy is indispensable for life, our method is highly transferable with only requirement of publicly accessible data. Our paper demonstrates the power of machine learning in understanding socioeconomic outcomes when the census and survey data are costly or unavailable.


Authors who are presenting talks have a * after their name.

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