A spatially explicit model of postdisaster housing recovery

Ali Nejat, Roxana J. Javid, Souparno Ghosh, Saeed Moradi

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Although postdisaster housing recovery is an important player in community recovery, its modeling is still in its infancy. This research aims to provide a spatial regression model for predicting households’ recovery decisions based on publicly available data. For this purpose, a hierarchical Bayesian geostatistical model with random spatial effects was developed. To calibrate the model, households’ data that were collected from Staten Island, New York, in the aftermath of Hurricane Sandy were used. The model revealed that on the scale of census tract, residents with higher income or larger household size were significantly less likely to reconstruct. In contrast, odds of reconstruction rose with increase of long-term residents. The model outputs were also employed to develop a reconstruction propensity score for each census tract. The score predicts probability of reconstruction/repair in each tract versus others. The model was validated through comparison of the propensity scores with the distribution of Community Development Block Grant Disaster Recovery assistance and its resultant reconstruction. The validation indicated capability of the model to predict the potential hotspots of reconstruction. Accordingly, the propensity score can serve as a decision-support tool to tailor recovery policies.

Original languageEnglish
Pages (from-to)150-161
Number of pages12
JournalComputer-Aided Civil and Infrastructure Engineering
Issue number2
StatePublished - Feb 1 2020


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