Inside Property Finder’s Home Valuation feature
Building AI Systems that Bring Clarity to Real Estate Decisions
Buying a home starts with a deceptively simple question: Is this property fairly priced? In practice, answering it means navigating incomplete information such as sale prices that may reflect outdated market conditions, asking prices that leave room for negotiation, and anecdotal advice. Formal valuations exist for compliance, but they typically arrive after decisions have already been made.
Property Finder’s Home Valuation feature helps close that gap by delivering data-driven home value estimates along with a six-month forecast. This gives buyers and sellers a clearer starting point when thinking about price.
This article offers a behind-the-scenes look at how our engineering and data teams at Property Finder design AI systems for real-world decision-making – balancing statistical rigour, market reality, and trust when certainty is never guaranteed.
The Home Valuation feature is a tool designed to support decisions early on, before formal steps like valuations or negotiations begin. Our approach follows a simple principle: we start with sale prices of similar homes, factor in what makes each property unique, sanity-check against real-world market conditions, and act conservatively when confidence is low.
We do not claim certainty. Instead, we focus on being transparent and genuinely helpful.
Valuation as Structured Inference
Property valuation is an inference problem complicated by sparse data, delayed feedback, and strategic behaviour.
The true market value of a property is never directly observed. Formally, we estimate a latent variable:
where Pi,t is the eventual transaction price and It is the information set available at time t.
Two key challenges shape this process.
First, information comes with delays. Transactions often close months after pricing decisions are made, and many segments exhibit low liquidity. Second, markets do not stand still. Demand rebalances, credit conditions move, and pricing behaviour changes over time. A system designed for one market environment will not work the same way as conditions evolve. For example, the COVID pandemic caused major shifts across many markets, meaning models trained on pre-pandemic data do not reliably carry over to post-pandemic price dynamics without explicit adaptation.
As a result, valuation is not a single prediction task. It is an AI system composed of interacting components, each responsible for a distinct function when signals are incomplete, delayed, or conflicting.
Comparable Pricing as a Local Prior
Real estate prices are fundamentally local. Even within the same building, two properties can transact at meaningfully different levels due to differences in size, layout, timing, or buyer mix. Global averages tend to smooth away exactly the variation that determines real outcomes.
For this reason, comparable pricing is treated as a local prior rather than a direct valuation estimate.
Observed sale prices are interpreted as outcomes generated by nearby, similar properties under current market conditions. Similarity is defined along three axes: physical proximity, structural characteristics, and recency. Together, these dimensions determine which transactions are informative for a given property at a given moment in time.
Instead of selecting a single “best” comparable, we construct a local set of recent transactions and summarise their behaviour as a distribution of plausible prices. From this distribution, we extract signals such as the typical price level, the degree of dispersion, and normalised measures like price per unit area. More recent transactions are weighted more heavily to reflect changing market conditions.
This step grounds the system in actual market behaviour before any adjustments are applied. It does not attempt to explain why prices differ across properties. Its role is narrower and more disciplined: to establish what the market has actually paid for homes most similar to the one being evaluated.
Modeling Deviations from the Local Prior
Comparable pricing alone is insufficient. Individual properties deviate systematically from local norms.
We model the final estimate as:
where Δ(xi) represents a learned adjustment informed by the full set of property attributes, and captures residual uncertainty that cannot be resolved from available data.
The adjustment term is not restricted to linear effects or additive assumptions. It is learned from historical outcomes using models capable of capturing threshold effects, interactions, and diminishing returns across attributes such as floor level, property size and layout. This allows the system to learn structure beyond simple parametric models.
This step-by-step approach reflects how experienced practitioners reason in practice. Start from observed market behaviour. Account for meaningful differences.
Empirically, separating local market structure from property-specific deviations materially reduces estimation error relative to approaches that collapse all signals into a single predictive step. More importantly, it produces estimates that remain stable under changing conditions and align with expert intuition, which is essential for trust.
In held-out evaluation, this approach achieves a median absolute percentage error of 4.7%. The improvement from baseline – from 11% error rate – is not driven by increased model complexity alone, but by the explicit separation of market-level structure from property-level variation. Encoding this hierarchy allows the system to learn efficiently from limited data while remaining robust to noise and regime shifts.
Signal Asymmetry and Guardrails
A common failure mode in valuation systems is treating all inputs as equally informative. This system does not. Transactions are treated as anchors. When sufficiently dense and recent, they dominate inference. Listings are treated as constraints, not targets. They bound plausible values:
Lmin ≤ Vi,t ≤ Lmax
particularly in low-liquidity segments, where L is derived from observed listing prices for comparable properties.
Market sentiment is treated as context. It informs directional pressure without dictating price levels. After inference, plausibility checks evaluate deviation from local distributions, sensitivity to sparse data, and consistency with recent activity. When uncertainty exceeds acceptable bounds, estimates are withheld entirely.
AI as a System, Not a Model
The intelligence of the system does not come from any single algorithmic choice, but from how learning, constraints, and uncertainty interact. Machine learning is used to extract structure from historical outcomes, while explicit rules and guardrails enforce consistency with market reality. This combination allows the system to adapt as conditions change without being misled by transient patterns.
In this setting, AI is not treated as an oracle. It is treated as an inference engine whose outputs must remain interpretable, bounded, and aligned with how real markets behave.
Forecasting Directional Pressure
Once a current value estimate is established, the next question is forward-looking. Markets often move before transactions reflect the change.
Predicting an exact future price at the property level is dominated by noise. Instead, we estimate directional pressure at the segment level.
Let Pt denote average price per unit area. We model absolute movement:
Δp = pt+k − pt
rather than percentage change, which improves stability and interpretability. Forecasts are monitored by segment and recalibrated on a fixed cadence to address market drift.
The output shows likely outcomes. It is designed to support reasoning about timing and risk, not to offer promises.
Why this matters
Real estate platforms are changing. Search is becoming conversational. Listings are becoming intelligence layers. Monetisation is shifting from ads to insights, transactions, and services.
AI isn’t an add-on to this transformation, it’s foundational.
Property Finder’s Home Valuation feature is one example of how we apply AI responsibly.It’s grounded in real data, guided by market reality, and designed to support people making real decisions rather than replace them. It’s a system where ground truth arrives late, signals conflict, decisions matter, and being approximately right and transparent is far more valuable than being precisely wrong.
The hardest problems in applied AI aren’t about model architecture, they’re about building systems that remain useful under uncertainty, admit when they don’t know, and prioritise trust over precision.
If these are the kinds of challenges that motivate you – working at the intersection of AI, statistics, market behaviour and trust – you’ll find space here to do some of your best work.
At Property Finder, we care deeply about craft, curiosity and intellectual honesty, and we support people to grow by tackling meaningful problems that matter in the real world.
If that sounds like the environment where you’d thrive, come build with us. Explore our open roles on our careers site.
