Estimation of Usable Area of Flat-Roof Residential Buildings Using Topographic Data with Machine Learning Methods

Abstract

The real estate appraisal largely consists of estimating the property’s value based on the transaction prices of similar buildings with the usable area being one of the main comparative units. A Polish appraiser finds data mentioned in the Price and Value Register (PVR). However, one of the authors’ previous studies indicated that the PVR contained highly incomplete information on usable area of residential buildings rendering it impractical for real estate appraisal purposes. Here, we propose a machine learning method to estimate the usable area of flat-roof residential buildings based on Light Detection and Ranging (LiDAR) data as well as the Database of Topographic Objects (BDOT10k). First, we train models with different architectures on the exact project data of residential buildings available online, obtained mostly from the design offices Lipińscy and Archon. Then, we apply trained algorithms on available residential building in Koszalin, Poland, using BDOT10k and LoD1 standard LiDAR data, and compare the results with usable area reported in PVR. Results show that the usable area of flat-roof houses without garages and extensions can be calculated with great accuracy up to 4%, while for more complex flat-roof buildings-up to 4–10%, depending on how detailed data are available. The model may be used by real estate appraisers to approximate the unknown usable area of residential buildings with known transaction prices, and as such increase the number of properties that can be compared to the evaluated real estate. To estimate the usable area of buildings with more complex roofs, a higher standard of LiDAR data is needed.

Keywords

• real estate appraisal
• giscience
• lidar
• linear regression
• neural networks
• spatial analysis