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
Interactive

The Real Estate Simulator

S01-S04
Four simulations, all assumptions disclosed
Time on market as throughput, pricing strategy scenarios, viewing costs and annual commission under uncertainty: set the sliders to the scale of your office. Every calculation path sits beneath its simulation.
Open all four simulations →
Our approach

No theory, just 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, so you can verify it.

  • 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, and listings in that window achieve +1.6-1.7%.
  • Redfin/VHT: professional photos sell 32% faster.
  • Rightmove, 2013: floor plans lift enquiries 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 are made.

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 emerge only 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 →