PM.
Market — Case study blueprint

UK Housing Market

Regional price, transaction and affordability trends built for evidence-led location comparison.

CSVPythonSQLPower BIData Modelling
Business problem

Headline house-price growth can hide low transaction volumes, poor affordability and major differences between local areas.

Objective

Create a regional market view that combines prices, sales activity and earnings-based affordability instead of relying on a single index.

Decision

Use a balanced scorecard of growth, liquidity and affordability.

01

Data source

UK House Price Index, Price Paid Data and ONS earnings-based affordability ratios.

Visit HM Land Registry & ONS ↗
02

Data preparation

  • Ingest monthly UK HPI and transaction data.
  • Standardise geographic codes across boundary changes.
  • Join ONS workplace-based earnings ratios at local-authority grain.
03

Data model

  • FactHousePriceIndex by geography-month-property type
  • FactTransactions and FactAffordability
  • DimGeography, DimDate, DimPropertyType and DimBuyerType
04

Analysis

  • Regional growth and liquidity matrix
  • Affordability trend and outlier detection
  • New-build versus existing-property comparison
05

Dashboard

Embed-ready dashboard area

Designed for Power BI, Tableau, Looker Studio or Plotly.

The live report will sit here after publication. Until then, the portfolio keeps a clear, truthful build status.

Discuss the project ↗
06

Key questions & KPIs

  • Compare price growth with transaction volume to spot thin-market movements.
  • Map affordability change by local authority and buyer type.
  • Separate new-build effects from the wider resale market.
Average sold priceAnnual price changeTransaction volumePrice-to-earnings ratio
07

Business recommendations

  • Use a balanced scorecard of growth, liquidity and affordability.
  • Treat provisional recent data as directional until later revisions arrive.
  • Drill from region to local authority before making location decisions.
08

Limitations

  • Recent UK HPI and transaction figures are revised as registrations arrive.
  • Affordability ratios do not capture deposits, mortgage rates or household circumstances.
09

Next steps

  • Automate the monthly data refresh.
  • Add local-authority maps and peer groups.
  • Document revision handling and attribution requirements.

From blueprint to published analysis.

Build the pipeline, validate the model, document the findings, then add the live dashboard and code.

Explore another project →