A machine learning method for real estate operation projects forecasting

ITPM 2024: pp. 77 - 89

Authors:

  1. Sergey Bushuyev
  2. Denis Bushuiev
  3. Nikolay Poletaev
  4. Mykola Malaksiano
  5. Dmitriy Kravtsov

1 Kyiv National University of Construction and Architecture, 31, Povitroflotskyi avenue, Kyiv, Ukraine

2 Kyiv National University of Construction and Architecture, 31, Povitroflotskyi avenue, Kyiv, Ukraine

3 Odessa National Maritime University, 34, Mechnikov street, Odessa, Ukraine

4 Odessa National Maritime University, 34, Mechnikov street, Odessa, Ukraine

5 Odessa National Maritime University, 34, Mechnikov street, Odessa, Ukraine

Abstract 

The study examined the impact of heterogeneous urban night lighting on the possibilities of
improving the forecast accuracy for real estate operations projects in a large city. By transforming
high-resolution night satellite images into light clusters, new dataset features were obtained after
calculating the centers of light clusters and the distances between them. The study used a dataset
on real estate rentals in Houston, Texas, USA. The light clusters were linked to the terrain based
on their geometric coordinates obtained using QGIS. These new features were integrated into a
machine learning model based on the LightGBM regressor. Calculations showed that the
reduction in forecast error (in the mean squared error metric, MSE) for our dataset was 11.8%,
significantly exceeding the influence of other features investigated in the study. The results
suggest that the “light” feature can be considered highly promising for real estate operations
projects.

Keywords

machine learning, artificial intelligence, computer vision, neural networks, real estate, project
forecasting, project modeling, satellite photos, geolocation, light clusters

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