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Data analysis · Market research

A pricing study of 50,000 European rental listings

The question was simple: where are rents mispriced, and where is demand strongest? Answering it meant cleaning and analysing more than 50,000 listings across European markets, then turning the findings into a report the team could act on.

Role
Market research and pricing analyst (freelance)
Client
European rental market project
When
2024
Stack
Python · pandas · NumPy · SQL · Data visualisation
System
  1. 50K+ listings
  2. Python pipelinepandas · SQL
  3. EDAregional variance
  4. Pricing report
  5. Expansion decisions

01

The problem

Listings came from many sources with inconsistent formats, currencies and missing values.

Expansion decisions were being made without a clear view of regional pricing or demand.

02

What I built

  • Python and SQL pipelines for extraction, cleaning and transformation of 50,000+ listings.
  • Exploratory analysis of pricing and demand by region and neighbourhood.
  • Reports and dashboards on demand patterns and competitive pricing, refreshed weekly.

03

How it works

  1. Step 01

    Extract

    Listings are pulled and stored with SQL.

  2. Step 02

    Clean

    Python (pandas, NumPy) fixes formats, outliers and missing values.

  3. Step 03

    Analyse

    Exploratory analysis compares pricing and demand across regions.

  4. Step 04

    Report

    Findings go into reports and dashboards for expansion decisions.

04

Outcome

listings analysed
0K+listings analysed
regional price variance found
0%regional price variance found
less time on data prep
0%less time on data prep
  • Pinpointed high-demand neighbourhoods for market positioning, with weekly trend monitoring.

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