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Real Estate & Property

Property Investment Scoring API

Gross yield is the number everyone quotes and the one that tells you least. The costs that decide whether a property works sit in the lines nobody models.

Buy-to-let investors
Property funds
Lenders
Investment platforms
Portfolio managers

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What your customers would see

example interface

MockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.

The problem this solves

A buy-to-let underwriting spreadsheet usually has rent, mortgage and a maintenance allowance expressed as a percentage someone picked. It omits void periods, letting fees, the service charge escalation on a leasehold flat, the cost of the energy-efficiency work that will be required before the property can legally be let in a few years, and the tax treatment that differs by ownership structure.

Those omissions are not rounding errors. On a modest-yield property, a four-week annual void and a service charge rising faster than rent can move net yield by a third. Investors discover this in year two, and lenders discover it when the stress test they ran turns out to have been optimistic about the expense line.

The appreciation side needs even more care. Local price growth over the next decade is genuinely unforecastable with useful precision, and any model presenting a single capital-growth figure is manufacturing false confidence. The defensible approach is scenario analysis — what the investment returns under a range of growth and rate paths — presented as a range of outcomes rather than a prediction.

Who buys this

Buy-to-let investors, Property funds, Lenders, Investment platforms, Portfolio managers

Category: Real Estate & Property

What the API returns

What comes back

Net yield after modelled expenses, alongside the gross figure for comparison
Itemised expense estimates: management, maintenance, voids, insurance, service charge, compliance works
Vacancy risk scored from local letting-market depth and tenant-demand indicators
Debt-service coverage under a range of interest-rate paths
Appreciation scenarios with explicit assumptions, presented as scenarios rather than a forecast
Cash-flow projection over a configurable holding period
Composite investment score with every component weight disclosed
Portfolio aggregation with concentration analysis by area, tenure and property type

Endpoints

Method	Path	Returns
POST	/v1/score	Score one property as an investment
POST	/v1/score/portfolio	Score and aggregate a portfolio
POST	/v1/scenarios	Run growth, rate and void scenarios on a property
GET	/v1/areas/{id}/fundamentals	Local yield, vacancy and demand fundamentals
GET	/v1/expenses/benchmarks	Expense benchmarks by property type and tenure
POST	/v1/cashflow	Cash-flow projection over a holding period

Request and response

illustrative

Request

{
  "property": {
    "address": { "postcode": "M4 5JN", "country": "GB" },
    "purchase_price": 215000,
    "property_type": "flat",
    "tenure": "leasehold",
    "lease_years_remaining": 86,
    "service_charge_pa": 1850,
    "bedrooms": 2,
    "epc_rating": "D"
  },
  "finance": { "ltv": 0.70, "rate_pct": 5.25, "term_years": 25, "interest_only": true },
  "holding_period_years": 10,
  "scenarios": ["base", "rates_up_200bp", "growth_zero", "void_8_weeks"]
}

Response

{
  "investment_score": 58,
  "band": "moderate",
  "rent": { "expected_pcm": 1180, "source": "rental_model", "range_80": [1120, 1245] },
  "yield": {
    "gross_pct": 6.59,
    "net_pct": 3.41,
    "net_after_finance_pct": 1.12
  },
  "expenses_pa": [
    { "item": "management", "amount": 1416 },
    { "item": "maintenance", "amount": 1290 },
    { "item": "voids", "amount": 1090, "basis": "3.8 weeks modelled" },
    { "item": "service_charge", "amount": 1850, "escalation_pct": 4.2 },
    { "item": "insurance", "amount": 240 },
    { "item": "epc_compliance_works", "amount": 5400, "one_off": true,
      "note": "estimate only; depends on survey" }
  ],
  "risk": {
    "vacancy_risk": 31,
    "dscr_base": 1.42,
    "dscr_rates_up_200bp": 1.03,
    "lease_risk": "monitor_below_80_years"
  },
  "scenarios": [
    { "scenario": "base",           "irr_10y_pct": 7.4 },
    { "scenario": "rates_up_200bp", "irr_10y_pct": 4.1 },
    { "scenario": "growth_zero",    "irr_10y_pct": 1.8 },
    { "scenario": "void_8_weeks",   "irr_10y_pct": 5.6 }
  ],
  "disclaimer": "Scenario outputs are modelled ranges, not forecasts, and exclude tax."
}

Where the data comes from

Transaction and valuation data	Purchase price context from registries and the valuation model, which anchors both yield and the capital side
Rental market data	Expected achieved rent and void expectations from the rental model, rather than an asking rent the investor found on a portal
Expense benchmarks	Management fees, maintenance rates, insurance and service charges by property type and tenure, assembled from managed-portfolio data and published schedules
Energy-performance records	Efficiency ratings, which in several jurisdictions now imply a dated and costly compliance obligation for lettings
Lease and tenure data	Lease length, ground rent and service charge terms, which can dominate the economics of a leasehold flat
Local demand indicators	Population, employment and household formation data from official statistics, which underpin the vacancy risk component

On data licensing. Being allowed to read a dataset does not automatically mean you may redistribute it, resell it through an API, or train a model on it. Before any build starts we check the licence, terms and derived-data rules of every source, and we design around the ones that do not permit your intended use. Where a commercial licence is required, that cost is identified during discovery rather than discovered later.

The intelligence layer

The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.

Yield computed from modelled achieved rent and itemised modelled expenses, never from an asking rent against a gross allowance
Expense estimation per line item against benchmarks conditioned on property type, tenure and area, with escalation modelled separately from level
Vacancy risk from local letting-market depth, time-to-let distributions and tenant demand indicators rather than a single area average
Monte Carlo simulation over rent, void, expense and rate distributions, so the output is a distribution of outcomes
Scenario analysis with every assumption exposed in the response, because a scenario whose assumptions are hidden is indistinguishable from a forecast
Explicit refusal to present a point capital-growth prediction; growth enters only as a scenario input the user or your product chooses
Backtesting of the expense and void components against realised outcomes from managed portfolios where that data is available
Monitoring on component drift, particularly expense benchmarks, which move with inflation faster than most model inputs

What it changes for the business

Underwriting that survives year two

Modelled voids, service charges and compliance works remove the surprises that break a spreadsheet case.

Comparable assessment

Properties in different areas and tenures become comparable on a consistent net basis.

Lender stress testing

Coverage under a range of rate paths is produced consistently across a book rather than per deal.

Faster screening

Investors can rank a long list on net fundamentals before spending time on viewings.

How it fits into your product

How it embeds

Single synchronous call for property-level scoring inside a listing or deal-screening flow
Portfolio endpoints for scheduled re-scoring and concentration reporting
Scenario endpoints exposed directly to end users so they can set their own assumptions
Component-level responses so your product can show the expense breakdown rather than only the score
PDF deal-summary generation where investors expect a document
Deployment inside your environment where borrower or client portfolio data cannot leave

Included in every build

Authentication, scoped API keys and per-customer rate limiting
Input validation with predictable, documented error responses
Versioned endpoints so your integration never breaks without notice
Request and response logging for audit and dispute resolution
Latency, error-rate and usage monitoring with alerting
OpenAPI specification, sandbox environment and client examples
Backups, failover design and a documented recovery procedure
An agreed support arrangement once the service is live

How we would build it

about 20 weeks

Discovery and feasibility

Weeks 1–3

This one depends heavily on data access, so we start by proving the sources exist, can be licensed for your use, and actually carry the signal the model needs. If they do not, you find out here.

Architecture and baseline model

Weeks 4–6

A written architecture and a baseline model measured against a holdout period, so you know what accuracy you are buying before the full build starts.

Production build

Weeks 7–16

Data pipelines, model training and serving, historical archive, validation harness, API, security, monitoring and documentation.

Validation and integration

Weeks 17–20

Backtesting against held-out history, accuracy reporting your team can audit, integration support and an agreed retraining cadence.

Commercial models

There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.

Fixed-scope prototype

A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.

Fixed-price build

An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.

Build then retainer

We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.

White-label product

The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.

Questions buyers ask

Do you forecast house price growth?

No. Ten-year local price growth is not forecastable to a precision that would make a point estimate responsible, and any model that offers one is selling confidence rather than information. Capital growth enters as a scenario assumption, chosen by you or your user, and the response reports the assumption alongside the result so nobody mistakes it for a prediction.

Is this investment advice?

No. The API returns modelled measurements and scenario outputs. It does not recommend buying, holding or selling. Whether your presentation of it constitutes advice or a financial promotion in your jurisdiction is a question for your compliance team, and we build to whatever constraints they set.

How reliable are the expense estimates?

Management, insurance and maintenance benchmark reasonably well. Service charges are property-specific and we use the actual figure where you supply it. Energy-compliance works are the weakest line by some margin — the cost depends on a survey of the actual building, and our estimate is a planning figure that can be wrong by a wide multiple on an older property.

Does it handle tax?

Not by default, and deliberately. Tax treatment depends on ownership structure, residence, other income and rules that change — modelling it generically would produce confident wrong answers. The outputs are pre-tax, labelled as such, and we can integrate your own tax logic if you have it and want to own it.

Can it score a portfolio rather than one property?

Yes, with aggregation and concentration analysis by area, tenure and property type. Concentration is usually the finding that matters: a portfolio of twelve flats in one development has a correlation profile nothing in the per-property scores reveals.

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