Data Reliability
How complete, consistent and trustworthy are the observed listings?
From raw listings to data quality, analytical populations, Gold marts, market intelligence and interactive dashboards.
How complete, consistent and trustworthy are the observed listings?
Which cities and neighborhoods are relatively more expensive or affordable?
How did asking prices and listing activity move across the available period?
How do location, area, age, floor, amenities and seller type relate to price?
Are phrases such as urgent and below market actually associated with lower asking prices?
Can observed listings be grouped into interpretable market segments?
Hot and cold markets are presented using the project's price and listing-activity framework.
City and neighborhood effects are separated from structural property characteristics.
Text signals are evaluated statistically rather than treated as proof of value.
The project keeps this distinction explicit throughout analysis and reporting.
Interactive market overview, quality analysis, geography, trends, price drivers and segmentation.
Reproducible analytical workflow. Run the notebook directly in Google Colab without local setup.
Dashboard contracts, measures, relationships and semantic notes.
Technical reports, QA notes, claims register and data dictionary.
ETL, notebooks, source code, outputs, tests and documentation.
The project distinguishes descriptive summaries, predictive contribution and observational association, with reliability gates and limitations documented.