OpenProp Property Market AI Analyst

AI property analyst for London, on real data

7 followers

AI property analyst for London, on real data

7 followers

OpenProp is an AI property analyst for the London residential market. Ask anything in plain English — "median flat price in Lewisham 2024", "is Tower Hamlets a buyers' market", "where can a £450k budget actually reach" — and get an answer computed live from 1.76 million HM Land Registry Price Paid records (Crown copyright). No scraped portal listings, no estimates, no hallucinated postcodes. Built for buyers, renters, planning consultants, and journalists who need numbers they can defend.

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Launch tags:SaaS•Artificial Intelligence•Data & Analytics

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When I first arrived in London a few years ago, one truth hit me immediately: the decision to buy or rent a home — one of the most important financial choices anyone makes in this city — is also one of the most opaque. For expatriates like me, the challenge is even sharper. We arrive full of hope, yet we have no intuitive sense of what a fair price looks like in a place so different from home. As a data scientist, I felt a deep personal calling to solve this real, everyday problem. In early 2021, working alone on my five-year-old MacBook in a small room at the Seraphine Hammersmith Hotel, I began writing the first Python scripts to analyse London's property market. Those early scripts were created for a small Hong Konger community news webpage. From them I produced simple, shareable PDF reports packed with clear insights. To my surprise and gratitude, the website analytics later showed that roughly one in every six Hong Kongers living in England had read those reports over the following years. That small statistic became my fuel. What started as a late-night side project for fellow newcomers had quietly begun touching real lives. Over time, that humble script grew into something far bigger. Today it has evolved into OpenProp: a conversational AI analyst that turns the official HM Land Registry's 1.76 million raw transactions (2010–present) into instant, chart-backed answers that anyone can understand in plain English. The Economics Behind It This project was never just about building another AI tool. It is the result of several deeply held convictions — economic, personal, patriotic, and a quiet but firm belief in justice through transparency. I am a firm believer in the Chicago School of economics, particularly the ideas of Milton Friedman and Ronald Coase. At its core, their thinking revolves around transaction costs — the hidden frictions of time, money, and uncertainty that prevent markets from working as efficiently as they could. In London's property market, those transaction costs are painfully visible: hours wasted hunting scattered data, expensive third-party reports that may be biased or outdated, and decisions made in the dark because official Crown copyright data — while publicly available — has always been difficult for ordinary people to analyse quickly and reliably. OpenProp was created to cut through exactly those frictions — lower information costs lead to better decisions, fairer markets, and a more efficient and equitable society. A Personal Commitment On a more personal level, I have always carried President John F. Kennedy's words in my heart: "Ask not what your country can do for you — ask what you can do for your country." As a Hong Konger who chose to build a life in the United Kingdom, I wanted to contribute, not simply receive. We Hong Kongers bring drive, resilience, and a deep appreciation for opportunity. Building OpenProp is one way I try to repay the welcome this country has given me. Radical Data Literacy Buying or renting in London remains one of the biggest financial decisions most people will ever make. Yet for too long, clear, accurate, up-to-date, and truly unbiased market data has been surprisingly hard to access. So I set out to democratise the official Crown copyright data once and for all. Every answer on OpenProp is generated from the same verified dataset that powers our fully open Kaggle notebooks. You can ask in plain English and receive not just words, but reproducible numbers and clear charts. There are no black boxes. The methodology is transparent. The calculations are verifiable. Supporting the Project The project was always designed with free access in mind. However, maintaining the data pipeline and delivering fast, reliable AI responses requires real time, skill, and resources. Every session pack purchased helps fund continued development and keeps the core experience open and accessible. If any part of this mission resonates with you, I warmly invite you to try OpenProp today. Ask it anything. Verify the numbers in our open notebooks. And if you spot something we can improve, please tell me. — Lorentz Yeung Founder, OpenProp

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3mo ago

Forum Threads

p/openprop-property-market-ai-analyst

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3mo ago

How OpenProp Works — London Property Data, Methodology & Limitations

2026-04-13 8 min read

Our Data Source

p/openprop-property-market-ai-analyst

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3mo ago

My System Beats Claude Sonnet 4.6 on Accuracy. Here's the Story.

I asked Claude Opus to cross-check my property article numbers. I found 16 discrepancies. I then verified each one against the Land Registry data manually with Python. Every single time, the result sided with my pipeline.

2026-04-19 7 min read

p/openprop-property-market-ai-analyst

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3mo ago

Rent vs Buy London: 10 Queries to Know If Buying Is Actually Possible

2026-04-17 6 min read

Your situation: You've been paying rent for years and wonder whether buying is realistic or something that keeps moving further away. This guide uses real Land Registry data to give you a concrete answer, and an interactive calculator to show whether your numbers work.

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