Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

RailEstate: An Interactive System for Metro Linked Property Trends

Chen-Wei Chang, Yu-Chieh Cheng, Yun-En Tsai, Fanglan Chen, Chang-Tien Lu

TL;DR

RailEstate addresses the need for metro-aware housing analysis by integrating spatial databases, long-term price data, forecasting, and an NL2SQL interface into a single web platform. It fuses a PostGIS-based backend with a React-Leaflet frontend, a LangChain-powered NL2SQL chatbot, and 25 years of Zillow data to deliver location-specific price insights, trend visualizations, and forward-looking forecasts. The system’s key contributions include a location-aware price engine, an interactive map with spatiotemporal visualizations, AI-driven price forecasting, and a natural-language querying workflow that democratizes access to complex geospatial data. This combination enables urban planners, investors, and residents to make data-informed decisions about transit-enabled housing markets in a real-time, user-friendly environment.

Abstract

Access to metro systems plays a critical role in shaping urban housing markets by enhancing neighborhood accessibility and driving property demand. We present RailEstate, a novel web based system that integrates spatial analytics, natural language interfaces, and interactive forecasting to analyze how proximity to metro stations influences residential property prices in the Washington metropolitan area. Unlike static mapping tools or generic listing platforms, RailEstate combines 25 years of historical housing data with transit infrastructure to support low latency geospatial queries, time series visualizations, and predictive modeling. Users can interactively explore ZIP code level price patterns, investigate long term trends, and forecast future housing values around any metro station. A key innovation is our natural language chatbot, which translates plain-English questions e.g., What is the highest price in Falls Church in the year 2000? into executable SQL over a spatial database. This unified and interactive platform empowers urban planners, investors, and residents to derive actionable insights from metro linked housing data without requiring technical expertise.

RailEstate: An Interactive System for Metro Linked Property Trends

TL;DR

RailEstate addresses the need for metro-aware housing analysis by integrating spatial databases, long-term price data, forecasting, and an NL2SQL interface into a single web platform. It fuses a PostGIS-based backend with a React-Leaflet frontend, a LangChain-powered NL2SQL chatbot, and 25 years of Zillow data to deliver location-specific price insights, trend visualizations, and forward-looking forecasts. The system’s key contributions include a location-aware price engine, an interactive map with spatiotemporal visualizations, AI-driven price forecasting, and a natural-language querying workflow that democratizes access to complex geospatial data. This combination enables urban planners, investors, and residents to make data-informed decisions about transit-enabled housing markets in a real-time, user-friendly environment.

Abstract

Access to metro systems plays a critical role in shaping urban housing markets by enhancing neighborhood accessibility and driving property demand. We present RailEstate, a novel web based system that integrates spatial analytics, natural language interfaces, and interactive forecasting to analyze how proximity to metro stations influences residential property prices in the Washington metropolitan area. Unlike static mapping tools or generic listing platforms, RailEstate combines 25 years of historical housing data with transit infrastructure to support low latency geospatial queries, time series visualizations, and predictive modeling. Users can interactively explore ZIP code level price patterns, investigate long term trends, and forecast future housing values around any metro station. A key innovation is our natural language chatbot, which translates plain-English questions e.g., What is the highest price in Falls Church in the year 2000? into executable SQL over a spatial database. This unified and interactive platform empowers urban planners, investors, and residents to derive actionable insights from metro linked housing data without requiring technical expertise.

Paper Structure

This paper contains 12 sections, 1 equation, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Description
  3. System Overview
  4. Function 1: Interactive Map for Location-Aware Price Visualization
  5. Function 2: Spatiotemporal Analysis of Housing Price Trends
  6. Function 3: Forecasting Future Housing Prices
  7. Function 4: Natural Language Interface for Housing Queries
  8. Comparison with Existing Tools
  9. Case Study: Real-Time NL2SQL Lookup for Historical Prices
  10. Scenario.
  11. Workflow.
  12. Conclusion

Figures (4)

  • Figure 1: System overview. Data pipeline with Supabase, OpenAI, FastAPI, and React.
  • Figure 2: Interactive map of metro stations and ZIP-code housing price bands. Basemap © CARTO
  • Figure 3: Popups for ZIP code (left) and metro station (right) showing price trends and forecasts.
  • Figure 4: Chatbot responses to valid queries (left) and unsupported queries (right).