Are you still compiling local and site-specific market prices manually?
Developers, intermediaries, and construction material suppliers often encounter moments when they need to determine whether the prices in a particular complex are rising or falling. However, most individuals still rely on the method of opening various real estate platforms one by one, scanning through property listings and prices, and manually transferring the information to Excel.
The issue lies in the fact that market prices fluctuate daily, and as the number of complexes increases, the time required for compilation also increases proportionally. Moreover, keeping up with community opinions through numerous posts becomes challenging for individuals. As a result, decision-making tends to be biased towards recent trends in a few complexes.
Hashscraper collects property listings, prices, and opinions regularly
Hashscraper collects and organizes property listings, prices, sales schedules, and mentions of complexes and locations in communities at specified intervals from real estate platforms. Instead of manually checking and transferring information each time, individuals can view the accumulated market trends for complexes and regions in one place.
The data collection items are designed based on specific needs. Below is an example of the typical field structure for property price data.
Example of actual crawling data
{
"collected_at": "2026-07-31",
"region": "경기 화성시 동탄",
"complex_name": "○○마을 아파트",
"area_type": "84A",
"listing_type": "매매",
"asking_price": 78000,
"price_unit": "만원",
"floor": "중층",
"listing_count": 12,
"prev_asking_price": 79500,
"price_change_rate": -1.89,
"source_platform": "portal_a"
}
The collected data, stacked in a time series format based on complexes, sizes, and transaction types, allows individuals to make judgments based on trends rather than specific values at a particular moment.
Use case: Evaluation of location and demand by a domestic developer
A domestic real estate developer wanted to assess the price trends, changes in property listings, and community interest in nearby complexes during the initial stages of considering a new business site. Initially, the responsible personnel manually compiled this information by region, but as the number of potential business sites increased, the compilation process became a bottleneck.
Hashscraper configured settings to periodically collect property listings, prices, sales schedules, and community posts for the target areas.
Crawling settings
- Collection targets: Real estate platform property listings and prices, sales announcements, local community posts and comments
- Collection frequency: Once daily for property listings and prices, twice daily for community posts
- Collection period: Ongoing during the business site evaluation stage (specific complexes and regions specified)
Analyzable items from crawling data
| Analysis item | Utilization |
|---|---|
| Price trends by complex and region | Assessing price trends by size and transaction type to determine upward or downward trends |
| Changes in property listings | Understanding the flow of supply through changes in the number of property listings |
| Sales schedules | Monitoring supply timing and competitive inventory through nearby sales announcements |
| Community interest | Comparing the relative size of demand interest based on mentions of complexes and locations in the community |
By consolidating the collected data, individuals can compare the direction of price movements and the level of community mentions for different potential business sites.
AI analysis: Categorizing community post topics and sentiments
Due to the large volume of community posts, it is challenging for individuals to read them all thoroughly. By adding AI analysis to the collected posts, it becomes possible to classify the topics and sentiments of each post, allowing for a structured examination of public opinions.
Example of AI analysis data
{
"post_id": "cm_20260731_00412",
"complex_name": "○○마을 아파트",
"topic": "교통/입지",
"sentiment": "positive",
"keywords": ["역세권", "GTX", "학군"]
}
Other scenarios for utilization
- Real estate intermediaries using summarized data on changes in property listings and price fluctuations in their assigned areas as consultation materials
- Construction material or interior design suppliers collecting sales and occupancy schedules to anticipate demand trends
- Asset managers referencing changes in community interest by region as an indicator during property evaluations
Conclusion
When evaluating real estate, having access to accumulated data on prices, property listings, and public opinions for various complexes and regions provides a more solid foundation than relying solely on recent trends in a few complexes. By categorizing community opinions based on topics and sentiments, individuals can confirm the direction of interest through data rather than reading every post.
The data collection focuses on publicly available property and post information, excluding personally identifiable information and only handling necessary items. Hashscraper takes care of crawler operation, maintenance, and monitoring for site changes, allowing individuals to focus solely on analysis.
Recommended Readings
- From crawling data to decision-making
- Outsourcing process for crawling — From inquiry to initial data, organized step by step
- Crawling cost estimates, why do they vary by company — Cost composition and hidden costs
