Data Science × Real Estate

AI for real estate agents: your data sells better
than your best gut feeling

6 modules. 6 problems every agent knows. 6 data-driven solutions that turn your CRM history, your portal statistics and public market data into concrete additional revenue - no new software, no disruption.

Total revenue potential per year
€0
For a brokerage with 32 sales agents · derived from documented assumptions
At a glance

Real estate numbers that speak for themselves

The starting point is a mid-sized brokerage: 32 sales agents, 450 transactions per year, focused on a major city plus surroundings.

6
Modules
1,350
Transactions analysed
€915,700
Revenue potential/year
4 wks
Pilot timeframe
The 6 modules

Each module solves a concrete brokerage problem

Click on a module to view the complete interactive notebook with code, visualisations and business impact.

Module 01
Time-on-market prediction
Gradient boosting predicts the marketing duration for every property - with a confidence interval. For the owner pitch that wins exclusive mandates.
€323,000
Additional revenue/year
XGBoostQuantile Regression
Module 02
Lead scoring & buyer matching
Random forest identifies from 14 behavioural signals which enquiries turn into buyers. The top 20% of leads contain 68% of all closings.
€206,000
Additional revenue/year
Random ForestLead Scoring
Module 03
Listing price optimisation
Not "What is it worth?" but "At which price do I maximise €/day?" Price-duration curves per micro-location reveal the optimal point.
€89,200
Additional revenue/year
OptimisationElasticity
Module 04
Acquisition prediction
Where will the market sell in 6 months? Portfolio and market data (holding period, demographics, market dynamics) surface the most promising micro-locations.
€127,500
Additional revenue/year
XGBoostPropensity Model
Module 05
Listing performance
Which photos, texts and ordering generate the most enquiries? Computer vision + NLP on 2,000 listings. Data-driven quality control.
€76,500
Additional revenue/year
CV + NLPA/B Testing
Module 06
Market-timing forecast
An LSTM time-series model forecasts demand and supply per district × property type 8 weeks ahead. List optimally instead of immediately.
€93,500
Additional revenue/year
LSTMTime Series
Revenue potential by module - full overview
Our approach

No theory - results in 4 weeks

We work with the data you already have. Your CRM, your portal statistics, public market data. No vendor lock-in.

🏠
Industry knowledge + data science
We understand acquisition, listings and owner conversations. Our models solve real brokerage problems - not academic exercises.
⚡
Results before perfection
Within 4 weeks we deliver a pilot dashboard with real numbers. You see the ROI before you invest. No 6-month concept phase.
🔒
Your data stays your data
GDPR-compliant, no cloud requirement, no sharing. The models run on your infrastructure - or hosted under your rules.
Evidence & sources

Where the industry figures come from

The levers of the model calculation follow published studies and case reports. Every third-party figure named in the text is referenced here - for verification.

  • Kreissparkasse Köln, 2023 (over 1,000 properties): +5% above market value sells in ~63 days, +10% only in ~281 days and at 97% of value.
  • ImmoScout24/Handelsblatt, 2023: ~12% of listings with a price cut.
  • Zillow Research: lingering overpriced properties sell ~5% below list; the best listing window sells ~2 weeks faster; listings in the best window achieve +1.6-1.7%.
  • Redfin/VHT: professional photos sell 32% faster.
  • Rightmove, 2013: floor plans lift inquiries by up to +93%.
  • SmartZip/RISMedia, 2022: predictive farming with a 4.6× lift over the base rate.
  • InsideSales: responding within 5 minutes qualifies leads 21× more often than after 30 minutes.
  • NAR Member Profile, 2024: median 10 transactions per agent per year (US).
  • immoverkauf24/Baufi24: customary commission 3.57% per side.
  • IVD: ~405,000 brokered deals, ~€95bn volume/year.
  • CBRE/QUIS: time on market in the 65-90 day band.
  • Destatis/industry statistics: ~600,000 housing transactions on ~43m homes per year (~1.6% turnover).

Figures from other markets and providers serve as plausibility anchors for the levers - they are not transferred to the sample brokerage. All euro amounts on these pages are model calculations on the assumptions named in the disclaimer. Sources retrieved: 2026-08-14.

Note on the context of this portfolio

The six modules use a fictional mid-sized brokerage (32 sales agents, 450 transactions per year, major city + surroundings) to show how data science and AI can be applied across the brokerage business - from time-on-market prediction to market timing.

All figures, datasets and results are entirely fictional. They serve solely to illustrate the methodology and the kind of impact that can be achieved - no promises.

In a real project, your brokerage's market knowledge is the decisive factor: What data lives in your CRM and portal statistics? How do acquisition and marketing run? Where is the biggest lever - exclusive mandates, lead quality or pricing strategy?

This portfolio shows which questions data science can answer in the brokerage business - the concrete answers only emerge with your data.

The first step to selling with data

A data workshop (1 day), a pilot (4 weeks), a result in euros. You see the value before you decide.

Book a data workshop →