How Unitrank works

A technical note on data sources, modelling, and known limits. Last updated April 2026.

Unitrank ranks every apartment by how much of your life it costs. Not just rent: commute, noise, grocery access, workspace quality, and every other daily activity. The apartment that costs you the fewest minutes per day ranks first.

Results are personalized to your work location, household, income, and lifestyle. Because we aggregate across all major platforms, the algorithm compares an Airbnb in Palermo against a direct rental on Argenprop, a temporary listing on Zonaprop, and an owner-listed apartment on MercadoLibre on equal terms: same cost model, same commute calculation, same noise score.

1. What we measure

Every apartment is evaluated across 21 life outcomes. Each is a daily or weekly activity your apartment makes easier or harder.

No single outcome dominates the ranking. The apartment that wins is the one where all 21 outcomes combined cost the least.

For each outcome, multiple strategies compete. An apartment with a pool may skip the gym commute for cardio. One without a desk factors in coworking costs. Cook at home, eat out, or order delivery: all three compete for every meal, and the cheapest wins.

For outcomes with multiple nearby options, trips are distributed across competing venues weighted by distance and quality. A closer, better-stocked supermarket captures more of your grocery trips than a distant corner store. Breakfast, lunch, and dinner are modeled separately with different venue preferences and household scaling.

2. What the algorithm catches

Four decisions the ranking makes differently than a human browsing listings.

Noise within the same building

A street-facing unit and a courtyard-facing unit at the same address can differ by 15-20 dB. That is the difference between sleeping with earplugs and sleeping in silence. Both units list the same rent. No platform distinguishes between them. The ranking knows which side of the building faces the avenida.

The commute triangle

A renter working in Microcentro, with a gym in Belgrano and kids at school in Núñez has three commutes, not one. "Palermo is central" is true in general but wrong for this household. The ranking finds the apartment that minimizes the triangle of all three destinations. Choosing Palermo because everyone says Palermo costs 20-40 extra minutes per day.

The expensas blindside

Two apartments at $800/mo rent. One has $50/mo in building fees, the other $250. The $800+$250 apartment is more expensive than a $900 apartment with $80 in building fees. Renters filter by rent. The ranking sorts by all-in cost.

Furnished vs unfurnished: the real comparison

Most platforms label apartments "furnished" or "unfurnished" but never compare them on equal terms. The ranking does. It calculates the true cost of an unfurnished apartment including furniture purchase, delivery, assembly, and disposal at exit. For stays under 12 months, unfurnished apartments frequently cost more than furnished ones once these hidden costs are included. Each user sees the comparison based on their actual planned stay duration.

3. All-in cost calculation

Most platforms show base rent. We show what you actually pay each month.

4. Commute model

Walking and driving distances use real routing on OpenStreetMap road data. These are actual walking routes, not straight-line distances.

Distances are pre-computed for 246 categories and 216k+ points of interest: supermarkets, subte stations, parks, gyms, schools, coworking spaces, and more. For custom locations such as your work address or school, exact routes are calculated at query time.

5. Noise model

Every apartment gets a noise impact score from 0 (silent) to 100 (major exposure).

Outdoor noise: 5 sources

• Road traffic: CoRTN road noise model using OpenStreetMap road geometry with calibrated traffic volumes. Major roads contribute most.
• Aircraft: Point-source attenuation from Aeroparque. Primarily affects Palermo, Belgrano, and Núñez.
• Nightlife: Nightclubs and bars within 200m. Relevant for Palermo Soho, San Telmo, and entertainment districts.
• Bus stops: Colectivo stops on high-frequency routes along major avenidas.
• Combined: All 5 sources summed in the power domain via acoustic addition.

Indoor attenuation

Outdoor noise is reduced by the building envelope. Closed windows with AC attenuate 14 dB. Open windows without AC attenuate 10 dB. Building age adds 0-3 dB, double-pane windows add 5 dB, and soundproofing adds 3 dB. Open-window values from Locher et al. 2018, a study of 102 Swiss residences.

Impact scoring

Formula: (indoor_dB - 35) / 30 × 100, clamped 0-100. The 35 dB baseline sits between the WHO sleep guideline of 30 dB and the ASHRAE office standard of 35 dB. The 65 dB ceiling is the WHO "major public health concern" threshold.

6. Converting money to time

All costs are converted to minutes per day using a formula that accounts for both earning power and psychological weight of spending:

minutes_per_dollar = √(labor_rate × perception_rate)

7. Statistical validation

We validate our cost model against market prices using hedonic regression, a statistical method that isolates the price premium for each individual feature.

• Method: Semi-log regression with hexagonal grid fixed effects, controlling for location at block level (~460m resolution)
• Features: 60+ amenities tested simultaneously: pool, parking, washer, elevator, AC, workspace, dishwasher, and more
• R²: 0.51, explaining 51% of price variation. In line with published hedonic studies
• Observations: 6,046 listings with complete data

Where our model agrees with market pricing, we have confirmation. Where we disagree, the algorithm finds value others miss.

Each arbitrage opportunity is personalized. The workspace advantage only appears when you specify remote work. The dog park advantage only appears when you indicate you have a dog. The algorithm adapts to your life, not to average market preferences.

8. Data sources

New sources are added regularly. Each new platform increases coverage and strengthens cross-platform comparison.

9. Refresh cadence

Data freshness directly affects ranking quality. Stale listings waste your time.

• Airbnb search: Every 2-6 hours. Detects new listings, price changes, and delistings.
• Airbnb availability: Calendar sync every hour, 300 listings per batch. Empty calendar means listing removed.
• Argenprop search: Tier-weighted rotation. A-tier barrios every ~1.2 days.
• LLM enrichment: Continuous batch processing. New listings enriched within 4-6 hours of discovery.
• Ranking rebuild: Incremental updates every 30 seconds. Full rebuild nightly.
• Trust scores: Refreshed on user feedback, listing age, and verification signals.
• Ranking computation: Personalized results in under 50ms. Evaluates all 21 life outcomes across the full inventory, selects the optimal strategy for each outcome per unit, and exposes every decision with a complete cost breakdown. Real-time, not pre-cached.

10. Known limits

The ranking is a model, not an oracle. Five areas where signal is weaker than the rest of the system and users should read results with that in mind.

• Furnishing detection is photo-based. Roughly 61% of units have detectable bed capacity from LLM photo analysis. The remaining 39% are treated as unknown, not as unfurnished. This is the best available signal when listing text is unreliable, but it misses units whose furniture is off-camera.
• Review and trust signals are uneven across sources. Airbnb has review counts, superhost badges, and guest-favourite flags. Argenprop, Zonaprop, and MercadoLibre do not. Trust scores on non-Airbnb sources fall back to listing age, agent volume, price-vs-market, and user feedback — a narrower set of inputs.
• Expensas estimation has a three-tier fallback. Actual expensas are used when the listing publishes them. Otherwise the model uses the median for other units in the same building, then the barrio average per square metre, then a percentage of rent. Each fallback is less precise than the one before. About 40% of direct rentals fall back at least one tier.
• Noise model omits indoor orientation. A courtyard-facing unit is typically 10–20 dB quieter than a street-facing unit in the same building. The model has fields for this (bedroom_faces, faces_street) but they are extracted from photos at only 14% coverage. For the other 86% the noise score reflects outdoor exposure without the orientation adjustment, so street-facing units inside quieter buildings look better than they are and interior units inside noisy buildings look worse.
• Hedonic R² is 0.51. The price model explains 51% of observed variation across 6,046 listings. The other 49% is noise, unobserved features, and listing-specific quirks (condition, dispute history, owner motivation). The ranking uses hedonic regression for validation, not for pricing, so the ceiling is not binding on the final score — but users who read "R² = 1" into price estimates should temper the expectation.

Each of these limits is actively being narrowed. The raw data is open. Errors are welcome: [email protected].

11. How to cite this work

Unitrank data is released under Creative Commons Attribution-ShareAlike 4.0. You may quote figures, embed tables, or republish datasets with attribution. Commercial use is permitted under the same licence terms.

Suggested citation

Unitrank (April 2026). Buenos Aires rental market data. Retrieved from unitrank.com/en/methodology. Licensed CC BY-SA 4.0.

For journalists and researchers

• Named entity: Unitrank on Wikidata (Q138792840).
• Bulk data: monthly snapshots at github.com/unitrank/public-data.
• Custom queries: barrio, stay duration, or household composition cuts available on request.
• Review copies: pre-publication fact checks within 24 hours for deadline-bound work.
• Attribution line: "Source: Unitrank, Buenos Aires rental market data, April 2026".

Questions about methodology: [email protected].