Level: Intermediate
Author(s): Dr. Satish B. Mohan; Dr. Alan Hutson
Venue: 2017 AACE International Annual Meeting, Orlando, FL
Abstract: The current mass appraisal models have not utilized real assessment data in their formulation, and the price variations have not been plotted on GIS maps. This paper presents a statistical appraisal model for housing, and maps the results to illustrate variations in home prices based on neighborhoods. It was previously reported that six variables had the greatest effect on the price of a home: frontage width, parcel depth, age, living area, architectural style, and neighborhood. The model used 2009 housing assessment data of 33,342 homes of a town in Western New York. The model had high accuracy with a R2 value of 0.9046, and tested well on validation. The analysis revealed that neighborhood exerts the maximum influence on housing price. This model can be used for mass appraisal of housing in towns with similar characteristics. The visualization provided by the GIS maps can help municipalities resolve inequities within and between neighborhoods.
Author(s): Dr. Satish B. Mohan; Dr. Alan Hutson
Venue: 2017 AACE International Annual Meeting, Orlando, FL
Abstract: The current mass appraisal models have not utilized real assessment data in their formulation, and the price variations have not been plotted on GIS maps. This paper presents a statistical appraisal model for housing, and maps the results to illustrate variations in home prices based on neighborhoods. It was previously reported that six variables had the greatest effect on the price of a home: frontage width, parcel depth, age, living area, architectural style, and neighborhood. The model used 2009 housing assessment data of 33,342 homes of a town in Western New York. The model had high accuracy with a R2 value of 0.9046, and tested well on validation. The analysis revealed that neighborhood exerts the maximum influence on housing price. This model can be used for mass appraisal of housing in towns with similar characteristics. The visualization provided by the GIS maps can help municipalities resolve inequities within and between neighborhoods.