What we are doing — and how we built it

1. Problem framing

Many households need to sell a home and rent another in the same season. Sequencing them poorly creates either a housing gap or months of double housing cost. The playbook treats the projects as parallel tracks with explicit coordination rules:

• Legal and dossier preparation starts immediately on both tracks
• The rental lease start date constrains the sale completion date
• A short overlap budget is accepted as insurance against a forced move
• One coordinator owns the shared calendar

2. Sale track (method)

Derived from standard Vaud practice for a co-owned or freehold residential sale:

1. Authority first — who signs (owner, POA, spousal consent under Art. 169 CC), plus fresh Land Registry evidence.
2. Dossier collation — title, PPE documents, insurance, plans, tax base for gains calculation.
3. Market testing — multiple written valuations before an exclusive mandate.
4. Offer discipline — written offers only; completion date left flexible enough for the move.
5. Notary completion — deed, tax withholding, vacant handover.

3. Rental track (method)

Vaud régies typically decide within days. The method therefore front-loads solvency evidence (especially for asset-based applicants without salary slips), then widens geography beyond the densest lake-station quarters:

• Universe: communes within ~15 minutes’ drive of Lutry or Pully station
• Preference: lower built-up share, daylight, open / lake outlook
• Exclusion: eastern Lavaux communes judged too far for the brief
• Channels: régies + portals + off-market network, scored with a visit card

4. Commune selection funnel

Before chasing listings, shortlist 3–4 communes with measurable screens:

1. Universe — Belmont-sur-Lausanne, Paudex, Pully, Forel (Lavaux), Lutry, Savigny, Bourg-en-Lavaux.
2. Data screen — settlement %, density, age structure, jobs per resident, tax coefficient, vacancy.
3. Map / terrain review — interactive leaflet map + Google Earth KML for slope, aspect, and visual fabric.
4. Field pass — drive each shortlisted commune at different times of day, then hunt properties only there.

5. Data & software pipeline

A small reproducible R pipeline joins public layers and writes the artefacts you see in the guide:

swissBOUNDARIES3D (swisstopo)
OFS Portraits des communes
Vaud communal tax coefficients
        │
        ▼
   R (sf + dplyr + leaflet)
        │
        ├── commune_map.html   (interactive choropleth)
        ├── communes.kml       (Google Earth)
        ├── points_of_interest.kml
        └── commune_stats.csv  (Excel-ready table)

Sources

• swisstopo swissBOUNDARIES3D — commune polygons
• OFS regional portraits — demography, land use, employment, vacancy
• Etat de Vaud arrêtés d’imposition — communal tax coefficients (snapshot year labelled in the CSV)

Reference points (stations, care facility) are approximate to ~100 m and exist only for orientation. The dashed radius on the map is a crude 8 km proxy for ~15 minutes by car — always verify real drive times in a routing app.

6. Interpreting the map

• Built-up % is the headline filter for “not too dense”, but it is a commune-wide average — use satellite / Google Earth for neighbourhood character.
• Jobs per resident separates daytime hubs from quieter dormitory communes.
• Tax coefficient matters for the tenant’s domicile taxes, not for landlord property tax.
• Vacancy % signals how hard the hunt will be; Lavaux figures are often very tight.

7. Anonymisation policy for this package

This public bundle deliberately omits:

• Personal names, birth dates, and contact details
• Street addresses, PPE lot numbers, and Land Registry identifiers
• Named advisors, notaries, banks’ relationship managers, and private dossiers
• Indicative sale prices tied to a specific property

What remains is process knowledge, public geography, and open government statistics — suitable to host as a working guide for other owners facing a similar parallel move.