CASE STUDY

AI Testing for Property Price Prediction Platform

End-to-end testing of an AI-driven property insights platform, validating deal analysis, price predictions, and user-facing property intelligence features.

REAL ESTATE

ABOUT THE PROJECT

Testing Property Intelligence System

The application is a property deal analysis platform that provides detailed insights for individual properties, including estimated value, investment potential, and supporting data points. Each property detail page presents AI-generated predictions alongside location, pricing trends, and property-specific attributes. The testing effort focused on validating how accurately and consistently these insights were generated, displayed, and aligned with input datasets.

HIGHLIGHTS

98%

Internal prediction accuracy observed

40%

Accuracy on external data validation

Validated property-level insight generation
Ensured consistency in UI-displayed data
Strengthened confidence in analytical outputs

Tools we Used

PROBLEM STATEMENT

Validating Property-Level Insights

The platform generates detailed property-level insights, including predicted prices and supporting metrics used for investment decisions. While the system produced consistent outputs internally, there was a need to verify whether these insights were accurate, reliable, and consistent when applied to real-world datasets. The challenge was to test both backend prediction logic and frontend data representation to ensure trustworthy outputs for end users.

OUR SOLUTION

Comprehensive Application Testing

Validated property detail page data rendering
Tested prediction outputs against input datasets
Verified consistency between backend and UI values
Assessed model performance across datasets
Performed Pandas-based analysis to compare actual vs predicted values and identify error patterns
Provided recommendations for improving accuracy and reliability

What we did?

UI Data Validation

Prediction Output Testing

Data Consistency Checks

Real Data Validation

UI Data Validation

We tested the property details page to confirm that displayed values such as predicted price, property attributes, and location insights matched the underlying data. This included validating field mappings, reviewing data accuracy, and checking whether users received consistent, relevant information across different property records and page states within the application.

Prediction Output Testing

We validated the predicted price shown on the property page by comparing frontend output with backend-generated model results. Using Pandas, we analyzed actual versus predicted values, reviewed error patterns, and verified that outputs remained consistent across different test scenarios. This approach helped confirm that the AI prediction logic was functioning correctly and that displayed values were accurately tied to the processed input data.

Data Consistency Checks

We checked consistency across EPC, sales, and postcode datasets to ensure the final outputs shown in the application were accurate after preprocessing and transformation. This testing helped verify that the data pipeline preserved correctness from ingestion to display, reducing the risk of mismatched values, broken mappings, or misleading property-level insights for users.

Real Data Validation

We tested the platform with external datasets to evaluate how well the prediction model performed in realistic conditions. This revealed a clear drop in accuracy compared to internal results, highlighting generalization issues. The findings helped identify reliability gaps and supported recommendations for improving feature selection, retraining strategy, and overall model trustworthiness.

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