How to Use Local Real Estate Listings to Create a Standout Market Research Project for Applications

Turn local listings into a portfolio-winning market research project — fast

Feeling stuck finding an internship-ready project? Use the real estate listings around you to build a rigorous, data-driven case project that proves analytical thinking, data visualization, and business communication — all the skills employers ask for. This guide shows step-by-step how to turn local listings (with concrete France and UK examples) into a standout portfolio piece for applications in 2026.

Why local real estate listings? Why now (2026)

Real estate is a perfect sandbox for student projects: it combines public data, clear KPIs (price, size, yield), geospatial context, and human stories. Since late 2024 and through 2025–2026, two trends make this especially powerful:

• Open data growth: The UK Land Registry and France's DVF transaction dataset have expanded API access and cleaner bulk exports, making transaction-level work easier to replicate and cite.
• PropTech and AI tools: New PropTech APIs, improved geocoding, and accessible AI for narrative summarization let students produce polished dashboards and written insights in days, not months.

Result: in 2026 interns are expected to show not just Excel fluency but a reproducible analysis with a clear narrative. A local market case project does exactly that.

What a strong project demonstrates (hiring manager checklist)

• Data sourcing & hygiene: You can gather, document, and clean real-world data.
• Analytical methods: You use appropriate statistical and geospatial techniques.
• Data visualization: You build charts and maps that communicate insight, not just numbers.
• Business impact: You translate findings into recommendations relevant to agents, investors, or local councils.
• Reproducibility & ethics: You document sources and respect privacy/GDPR constraints.

Choose a focused research question (with France & UK example prompts)

Start with a single, answerable question. Examples:

• France — Sète & Montpellier: What premium do sea-view properties command versus inland properties across the Languedoc between 2018–2025?
• France — Montpellier: How did renovated urban apartments perform in price per square metre after 2019 renovations compared with similar non-renovated units?
• UK — Acton, London: Do new high-rise developments (e.g., One West Point) trade at a premium for pet-friendly amenities and concierge services?
• UK — Dorset: How does distance to town centre/rail station correlate with price/yield for country homes (e.g., Higher Waterston) over the past decade?

Step 1 — Plan your scope and hypothesis

Define geographic boundaries (neighbourhood, postcode sector, city), time range (e.g., 2016–2025), and a short hypothesis. Example:

Hypothesis: In Sète and nearby Montpellier, properties with direct water or sea views commanded a 15–25% premium in 2021–2025 compared with similar-sized inland properties.

Keep scope tight. For a 4–6 week student project, one city or neighbouring pair (like Sète & Montpellier) is ideal.

Step 2 — Data sources you can use in 2026

Combine listings (supply-side) with transaction records (demand-side) and contextual layers. Trusted sources in 2026 include:

• Listings portals: Le Bon Coin, SeLoger (France); Rightmove, Zoopla, OnTheMarket (UK). Use portal APIs where available or export features; always check terms.
• Public transaction registers: France's DVF (Demande de Valeurs Foncières) and the UK Land Registry Price Paid Data — both improved accessibility since 2024 and offer transaction-level detail.
• Local planning & cadastral maps: France’s cadastre and municipal planning portals; UK local authority planning portals for development timelines.
• Geospatial & POI data: OpenStreetMap, Google Places APIs for distance to coast, stations, schools.
• Rent & yield sources: Idealista/Rentberry (EU), Home.co.uk (UK) rental estimates for yield calculations — combine listings and transaction datasets to cross-check.
• Macro data: INSEE (France), ONS (UK) for employment, population change, and household size.

Pro tip: cite dataset versions and access dates in your README — that’s part of reproducibility.

Step 3 — Collect and clean data (practical checklist)

1. List fields to collect: transaction date, sale price, property type, size (sqm), rooms, year built, listing description, postcode, latitude/longitude.
2. Use APIs or bulk exports when possible. For scraping, respect robots.txt and site terms; throttle requests and log timestamps.
3. Geocode addresses consistently (use postcode for UK, code INSEE or cadastre for France). Resolve duplicates — transactions vs listings can repeat for the same property.
4. Create a canonical unit: price per square metre (€/m² or £/m²). For small UK flats given in square feet, convert to m² for comparisons.
5. Normalize categorical fields (e.g., “sea view”, “vue mer”, “balcon”, “balcony”) so keyword flags are consistent.

Note on privacy: Do not publish personally identifiable information. Aggregate or anonymize transaction-level records before public release when data terms require it.

Step 4 — Analysis techniques that impress

Choose methods that match your question and skill level. Employers value correct application more than complexity.

• Descriptive statistics: Median price/m² by year, distribution charts, and time-series plots.
• Comparative analysis: Group comparisons (sea-view vs inland; renovated vs not) with t-tests or non-parametric equivalents.
• Hedonic regression: Model price as a function of size, rooms, age, distance to coast/station, and amenity flags to estimate premiums.
• Clustering: Use k-means or hierarchical clustering to identify neighbourhood profiles (luxury, family, commuter).
• Geospatial mapping: Heatmaps of price/m², distance-bands to rail or coastline, and mapped residuals from hedonic models to find over/under-priced pockets.
• Yield & affordability: Estimate gross yields using local rent data and compute price-to-income or price-to-rent ratios for affordability insights.

Tools: Excel or Google Sheets for starters; Python (pandas, geopandas, scikit-learn, statsmodels) or R (tidyverse, sf) for reproducible work; QGIS for mapping; Tableau/Power BI/Observable for dashboards.

Data visualization: what to show and how

Effective visuals tell the story at a glance. Use these combinations:

• Time-series line chart: median price/m² by year with shaded confidence bands.
• Boxplot or violin plot: price per m² distribution, segmented by neighbourhood or amenity.
• Map + choropleth: price intensity by block or postcode with pin overlays for notable listings (e.g., a Sète sea-view home).
• Scatterplot with regression line: size vs price, colored by renovation status.
• Small multiples: show the same metric across multiple nearby towns (Sète, Montpellier, nearby hamlets) for comparison.

Design tips: annotate big anomalies, use contrasting colours for comparisons (avoid rainbow palettes), and always label axes with units (€/m², £/m²).

Two brief case examples you can replicate

Case A — Sète + Montpellier seaside premium (France)

Goal: Estimate the percentage premium for properties with direct water or sea views between 2018–2025.

1. Collect transaction records from DVF and recent listings from SeLoger/Le Bon Coin for the Sète and Montpellier postal codes.
2. Flag “sea view” using listing descriptions and distance to the coastline (e.g., < 500m).
3. Run a hedonic regression: log(price) ~ log(size) + rooms + sea_view_flag + renovated_flag + year dummies + postcode fixed effects.
4. Interpret coefficient on sea_view_flag as the approximate percent premium, report confidence interval, and map clustered hotspots where the premium is highest.

Deliverables: a 6-slide deck with methods, result summary (e.g., “Sea view premium estimated at +18% [CI: 12%–24%]”), 2 maps, and a reproducible notebook on GitHub.

Case B — Acton’s amenity premium (UK)

Goal: Quantify whether new developments with pet-focused amenities (indoor dog parks, salons) attract listing-price premiums in Acton.

1. Gather recent listings for Acton developments and neighbouring postcodes from Rightmove/Zoopla, capture amenity mentions.
2. Use clustering to segment properties by building age and amenity set, then compare median prices and yields across clusters.
3. Supplement with commuter accessibility scores (distance to station + Tube lines) as control variables.

Deliverables: interactive dashboard showing amenity clusters, a short policy memo for a fictional developer recommending which amenities carry measurable premiums.

How to present it in applications (resume, cover letter, portfolio)

Hiring managers scan quickly. Put the most concrete impact front-and-centre.

Resume bullets (use active metrics)

• Built a reproducible market research case study using 4,200 transactions (DVF + listings) to estimate a 18% sea-view premium in Sète–Montpellier (2018–2025).
• Created an interactive dashboard visualizing price-per-m² heatmaps and amenity clusters using Python, QGIS, and Tableau; reduced analysis time by 60% with automated ETL scripts.

Cover letter / application pitch

One-sentence hook: “I used DVF and local listings to build a market research case estimating amenity-driven price premiums in Sète and Acton — I’d be excited to apply the same approach to your market research team.”

Attach a one-page executive summary and link to the GitHub repo or live dashboard. If confidentiality prevents full dataset release, include snapshots and a detailed methods appendix.

Portfolio & deliverable checklist (what employers want)

• One-page executive summary: key insight, headline metric, recommended action.
• Slide deck (6–8 slides): context, methods, result, map/chart, recommendation, limitations.
• Interactive dashboard or static visuals: embed images if you can’t publish the dashboard.
• Reproducible code notebook: Jupyter/Observable/RMarkdown with clear README and data source citations.
• Data provenance file: list datasets, access dates, and any transformations.

Ethics, licensing and legal notes

Respect dataset licenses and site terms. Key 2026 considerations:

• GDPR: do not publish names or contact info. Aggregate or anonymize transaction records if required.
• Site scraping terms: many listing portals restrict scraping; prefer official APIs or bulk downloads where available.
• Attribution: cite public datasets (DVF, Land Registry) and include links in your README.

Advanced strategies to stand out in 2026

Once you’ve completed a baseline project, add one of these to differentiate:

• Satellite & street imagery: use Sentinel-2 or Google Street View to add building condition or green-space metrics.
• Time-series forecasting: build short-term price forecasts and show scenario analyses (interest rate rises, policy shocks).
• LLM-powered narratives: use a generative model to produce a succinct market brief, then validate claims against your data (employers like thoughtful, not overtrusted, AI use).
• Agent-facing deliverable: convert findings into a one-page sell sheet a local agent could use — shows you can translate analysis into revenue impact.

Project timeline & quick roadmap (4–6 week plan)

1. Week 1: Define question, collect sample datasets, write project plan.
2. Week 2: Clean and geocode data; exploratory visualizations.
3. Week 3: Run core analyses (comparisons, regression, mapping).
4. Week 4: Build visualizations, write executive summary, prepare slides.
5. Week 5: Add polish — dashboard, reproducible notebook, and ethics/license notes.
6. Week 6: Prepare application assets (resume bullets, cover letter text, portfolio upload) and solicit feedback from peers/mentors.

Common pitfalls and how to avoid them

• Pitfall: Mixing listing prices with sale prices without adjustment. Fix: clearly label and, where possible, use transaction records for realized prices.
• Pitfall: Small sample bias in micro-markets. Fix: expand time window or aggregate neighbouring postcodes for robustness checks.
• Pitfall: Overclaiming causation. Fix: use language like “associated with” and include limitations; use fixed-effects or difference-in-differences where appropriate.

How to talk about the project in interviews

Use the STAR framework, but focus on impact:

• S — Situation: the local market had noisy signals; you needed to quantify premiums.
• T — Task: estimate amenity-driven price effects and produce an actionable brief.
• A — Action: data collection, hedonic regression, geospatial mapping, dashboard delivery.
• R — Result: headline metric (e.g., +18% premium), created a dashboard now used as a portfolio asset, and presented recommendations to a local agent or professor.

Final checklist before submitting your project

• Is the question clearly stated and motivated?
• Are data sources listed with access dates and license notes?
• Are methods documented and code reproducible?
• Is the headline insight concise and supported by visuals?
• Do you have a 1-page summary and a GitHub link or hosted dashboard?

Closing — turn local listings into career momentum

Market research projects built from local listings let you show exactly what employers want in 2026: reproducible data work, clear visual storytelling, and business-relevant recommendations. Whether you analyse sea-view premiums in Sète, amenity value in Acton, or affordability in Dorset, a well-scoped project is a powerful portfolio piece for internship applications.

Ready to start? Pick a neighbourhood, write a one-paragraph hypothesis, and collect your first 100 records this week — then iterate. Small, well-documented wins beat grand ideas without evidence.

“A focused, reproducible local market case study is one of the fastest ways to turn classroom skills into an internship-ready portfolio.”

Call to action

Start your project today: publish a short summary and visuals on GitHub or a personal portfolio, and add the headline metric to your resume. Need templates — slide deck outlines, resume bullets, or a notebook starter? Download our free project kit at internships.live/resources or message us and we’ll send the templates and a 4-week roadmap tailored to France or UK markets.

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