How AI Chooses the “Best Places to Live” (And What It Really Looks For)

The way people research locations is changing. Traditional “best places to live” lists were usually created by editors who selected a set of measures, decided how important each one should be and then ranked towns or cities accordingly. In 2026, buyers are also using generative AI tools, AI-powered search experiences and property platforms to compare locations much more quickly.

That speed is useful, but it can create a false impression of certainty. An AI answer may look decisive even when it is based on mixed dates, incomplete local data or assumptions that do not match the buyer’s real priorities. A recommendation is therefore better treated as a structured shortlist than as a final verdict.

At Farrell Heyworth, we increasingly see buyers arrive with data-led questions: Which area offers the strongest balance of price and commute? Where are first-time buyers getting more for their budget? Which locations appear to have momentum? These are sensible questions—but the quality of the answer depends on how the evidence is selected, weighted and interpreted.

The First Thing to Know: There Is No Single AI “Best Places” Formula

The phrase “AI ranking” can describe several different systems. They do not all work in the same way, and they do not necessarily use the same evidence.

Google explains that its generative Search features can use retrieval-augmented generation to find current pages from the Search index and “query fan-out” to explore related subtopics. In practice, a question such as “Where should a family live within 45 minutes of Preston for under £250,000?” can trigger separate lines of research around prices, journey times, schools, amenities and available homes. See Google Search Central’s guide to generative AI features.

A 2026 Data Snapshot: The Same Region Can Tell Very Different Stories

Official house-price data shows why a single headline ranking can be misleading. The following provisional Office for National Statistics figures relate to May 2026 and compare three important North West markets.

Sources: ONS local housing data for Preston, Lancaster and Blackpool. Figures are provisional, local estimates can be revised, and short-term local changes can be more volatile because they are based on fewer transactions.

How an AI System Builds a “Best Place” Recommendation

Although systems differ, a robust location comparison usually follows six broad stages.

The Main Signals AI Looks For

1. Affordability and the Real Cost of Living There

House price is usually one of the strongest signals, but price alone is a weak measure of affordability. Better models compare prices with local earnings, likely mortgage costs, rents and the type of home a budget can actually buy.

The latest ONS housing-affordability release reported that the median house price in England was 7.6 times median workplace earnings in 2025. The ONS uses five years of earnings as a broad affordability threshold, which illustrates why price-to-income measures can be more informative than a headline average price. See the ONS housing affordability data.

A useful AI comparison should also consider deposit requirements, council tax, energy performance, service charges where relevant, insurance, maintenance and commuting costs. A cheaper home can be less affordable overall if it creates significantly higher monthly outgoings.

2. Price Movement, Demand and Market Liquidity

AI may compare annual price changes, transaction volumes, listing supply, time on market, price reductions and the relationship between asking and achieved prices. These signals help indicate whether demand is strengthening, weakening or remaining stable.

However, rapid price growth does not automatically make a place better to live. It may indicate improving demand, but it can also reduce affordability and make entry harder for first-time buyers. Equally, slower growth can reflect stability rather than weakness. The meaning depends on the buyer’s objective.

3. Transport, Commute and Access to Essential Services

Connectivity is broader than the distance to the nearest railway station. A serious comparison may consider door-to-door journey time, service frequency, road access, congestion, walking and cycling routes, and access to hospitals, shops, schools and employment centres.

The Department for Transport’s journey-time statistics are built from public transport timetables, road, cycle and footpath networks, population data and service locations. This shows how accessibility can be measured rather than guessed. See the Department for Transport journey-time statistics.

Hybrid working has made the weighting more personal. Someone travelling twice a week may accept a longer journey in exchange for more space, while a daily commuter may prioritise reliability and frequency above almost everything else.

4. Schools and Education

For families, AI may look at school performance, inspection information, distance, admissions areas and the range of provision available. The Department for Education provides official datasets and performance tools through Explore Education Statistics.

School data needs careful interpretation. A strong result from one year is not a guarantee of future performance, an inspection judgement is not the same as a complete measure of fit, and living nearby does not guarantee admission. The child’s needs and the actual admissions policy remain critical.

5. Crime and Perceived Safety

AI systems can access street-level crime and policing data, but a raw count can be misleading. Busy town centres, nightlife areas and transport hubs may record more incidents because more people pass through them. Boundaries, reporting patterns and population size also affect comparisons.

The official Police.uk open-data service provides downloadable street-level crime, outcomes and stop-and-search data. A responsible ranking should examine trends and rates in context rather than treating one month’s total as a definitive safety score.

6. Amenities, Healthcare, Green Space and Everyday Convenience

Distance to supermarkets, GP practices, hospitals, parks, leisure facilities, cultural venues and town centres can all contribute to liveability. Walkability and the ability to reach everyday services without a car may matter more to some households than a marginal difference in house-price growth.

This is also where broad rankings often miss detail. Two neighbourhoods within the same town can have very different access to schools, green space, parking or public transport, even though the town-level data is identical.

7. Digital Connectivity and Working From Home

Broadband availability, mobile coverage and the suitability of housing for home working are increasingly relevant. An area can look attractive on price and commute while being a poor fit for a household that needs reliable high-speed connectivity or a dedicated workspace.

8. Housing Quality, Environmental Risk and Future Development

A sophisticated comparison may include property age, energy efficiency, flood exposure, air quality, planned development and regeneration. These factors can affect running costs, insurability, future convenience and long-term desirability.

They are also difficult to compress into one score. Regeneration can bring new transport, homes and amenities, but construction disruption and the timing of delivery matter. Flood or planning risks may vary at property level rather than town level. AI can flag the issue, but the buyer still needs to verify it against the specific address.

An Illustrative AI Scoring Model

The table below shows how one buyer-focused model could be constructed. It is an example only—not a disclosed formula used by Google, Farrell Heyworth or any particular property platform.

A first-time buyer could increase the affordability weighting. A family might increase schools and green space. A commuter could increase transport. The ranking changes because the question changes.

Why AI Rankings Can Feel Different From Traditional Reputation

AI-led recommendations can bring lesser-known locations into view because the system is comparing measurable performance rather than relying only on long-established reputation. A town with lower prices, improving connections and steady demand may appear ahead of a more prestigious area that scores poorly on affordability.

That can be genuinely useful. Data can challenge assumptions and help buyers consider places they may previously have overlooked. It can also reveal trade-offs that traditional league tables hide—for example, a location may offer a shorter commute but less space, or better affordability but fewer services within walking distance.

The risk is that a polished AI answer can make a judgement look more objective than it really is. Every ranking still contains choices: which locations were included, which evidence was available, how old it was, how factors were weighted and what the system assumed the user meant by “best.”

Where AI Can Get It Wrong

The Information Commissioner’s Office distinguishes fairness from simple mathematical consistency and highlights the need to consider bias and trade-offs in AI systems. Its guidance is a useful reminder that data-led does not automatically mean neutral. See the ICO guidance on fairness in AI.

AI Ranking vs Local Knowledge: Why Both Matter

AI is strongest at processing broad evidence quickly. Local expertise is strongest at explaining what the averages miss. The best buying decisions combine both.

An AI answer can tell a buyer that two areas have similar average prices. A local agent can explain why the homes available at that price may be completely different, where demand is concentrated, which compromises are typical and how quickly suitable properties tend to attract interest.

A Better Way for Buyers to Use AI in 2026

The Farrell Heyworth View

AI is becoming a valuable discovery tool for property buyers because it can compare more evidence, more quickly, than a person could review manually. It is particularly useful for broadening a search, identifying overlooked locations and exposing the trade-offs between price, access and lifestyle.

It should not, however, be treated as an automated instruction to buy in a particular town. The most reliable process is to use AI for structured research, official data for verification and local expertise for interpretation.

In 2026, the “best place to live” is not a universal location at the top of a national table. It is the place that best fits a specific household’s budget, daily life, priorities and long-term plans—and that conclusion should remain open to challenge as the evidence changes.

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