In today’s dynamic real estate market, accurately assessing the price of different zones within a city is essential for real estate professionals. However, this task has traditionally been challenging and time-consuming. To address this issue, our team at AI Superior has developed a deep learning-based solution for a leading real estate online platform. By employing a semantic segmentation approach, we have created a system that analyses maps of cities, identifies and classifies various areas, and extracts valuable insights to assess the price of zones accurately.
Harnessing Deep Learning for Urban Zone Pricing Analysis
Summary
Challenge
A leading real estate online platform required a solution that would facilitate the value assessment of urban zones within a city. To provide industry professionals with more accurate insights, our segmentation model was proposed as a potential tool to assist in this process.
Solution by AI Superior
To tackle this challenge, our team at AI Superior embarked on a project to develop a solution that facilitated price assessment of areas in a city.
The initial stage of our project involved analyzing the map of the city in question. Employing a semantic segmentation approach, we generated a dense segmentation map of the area where each pixel was assigned to one of the following predefined classes: roads (including paved, highway, and byroad), residential houses (including small houses and multistorey buildings), infrastructure (including commercial areas and ongoing development/construction works), green areas (comprising trees), and open land areas (available for potential real estate development). This comprehensive analysis provided a detailed understanding of the city’s layout and composition.
To gain a deeper understanding of the city’s evolution over time, we incorporated historical maps from different years into our analysis. By comparing and contrasting these maps, we were able to extract insights and statistics regarding the city’s development. Our analysis was primarily focused on monitoring the availability of open areas for potential realty development over time. These temporal insights proved invaluable for assessing the value and desirability of specific regions within the city and allowed the real estate company to apply their expertise and market knowledge to determine appropriate price evaluations.
Outcome and Implications
The implementation of our system provided the real estate company with valuable insights and data to facilitate more efficient price assessments. By visualizing and understanding the composition of different zones, the company could apply their expertise and market knowledge to determine appropriate price evaluations. As a result, they were able to offer improved services to their users, ultimately contributing to a more efficient and informed real estate market.
This project exemplifies AI Superior’s commitment to empowering industries with cutting-edge AI technologies. With a proven track record of successful projects, we continue to drive innovation and shape the future of real estate assessment.
