A valuation model built on 12,115 real listings across 12 localities of Bogotá. Estimate fair rental prices and investment property values — with transparent methodology and honest uncertainty ranges.
Enter property details and get an estimated monthly rent range based on your locality's market benchmarks.
Estimate rental price →📈Find out what a property should cost to meet your target rental income, or what income to expect from a given price.
Calculate investment →🏦Model down payment, loan term and closing costs for new or used properties, add rental income, and compare multiple buying options side by side.
Compare mortgage options →Each of the 12 localities has a calibrated rental price-per-m² range derived from real market listings. This is the base signal of the model.
Colombia's 1–6 socioeconomic stratum system correlates with property quality and demand. The model applies a multiplier from 0.72× (estrato 1) to 1.48× (estrato 6) on top of the locality baseline.
Each bedroom beyond the first adds a locality-tier-adjusted premium. Amenities (parking, gym, pool, doorman) add calibrated flat premiums derived from listing differentials.
The model outputs a ±12% range around the point estimate, reflecting real observed variance in the data. The true market price sits inside this range in ~75% of comparable listings.
Each locality has a calibrated average annual yield (rental income / property value). The investment calculator uses this yield to convert between monthly income and property value.
The dataset contains 12,115 real listings as of Q2 2026, covering 12 localities of Bogotá where sample size was large enough to be statistically meaningful. It captures apartments only (not houses or commercial properties). Listings reflect asking prices, not final transaction prices.
This is a transparent rule-based estimator calibrated from real listing data — not a trained statistical model, so there is no formal R² to quote. The ±12% band exists precisely because floor level, views, building age, and micro-location are not captured here and can shift the real price meaningfully within that range.
A more capable version of this is already running locally: a Random Forest model trained directly on the raw scraped listings (not pre-aggregated ranges), with separate models for apartments and houses, plus a full ROI/cap-rate pipeline for investment properties. The next step is bringing that model online here, and using an LLM to read unstructured listing descriptions — extracting renovation quality, view, and condition signals that today's structured fields miss — to shrink the ±12% band further.
Yes — completely free to use, no login required.
It is a transparent, rule-based estimate calibrated from real listing data per locality and stratum — not a trained statistical model, so there is no formal accuracy score to quote. It is a reference guide, not a certified appraisal; treat the ±12% range as a deliberate buffer for floor, view, age, and condition, which are not modeled.
Rental yield is the annual rental income divided by the property's purchase price, expressed as a percentage. A 6% yield means a COP 500M property generates COP 30M/year in rent (COP 2.5M/month).
Colombia classifies residential properties from 1 (lowest) to 6 (highest) based on socioeconomic conditions. Estrato affects utility subsidies, taxes, and is a strong predictor of rental price.
The dataset is available on request for research or professional use. Send an email explaining your use case and I'll be in touch.
Currently the model covers 12 localities of Bogotá — only those with enough listings to be statistically reliable. Other cities and the lower-data southern localities are not yet supported.