Real Estate · Technology · Analytics

Dominic Mayne Real Estate Graduate & Proptech Builder

BSc Real Estate, University of Reading (2:1). I built this platform independently to demonstrate that real estate knowledge and modern technology aren't separate disciplines — they're most powerful when combined.

Live Platform
✦ AI Market Commentary · Claude

Background
Why I built this
A real estate degree meets modern technology — built independently, from scratch.

I graduated from the University of Reading with a BSc in Real Estate (2:1), with modules covering Real Estate Valuation, Development Appraisal, Applied Property Law and Asset Management. My final project received a First Class grade.

During my studies and job search I noticed a gap: the tools used by institutional property professionals — CoStar, EG Propertylink, agent research platforms — are expensive, complex, and largely inaccessible to graduates. I wanted to understand how modern technology could democratise access to this kind of intelligence, so I built my own version.

This platform was built entirely independently, with no prior professional software development experience. I taught myself Python, FastAPI, machine learning with scikit-learn, and frontend development to bring it to life. Every line of code, every design decision, and every data point was my own work.

I'm now looking to bring both sides of this — real estate knowledge and technical capability — into a graduate role at a firm where data, technology and property intersect.

2:1
BSc Real Estate, Reading
8+
Submarkets Tracked
1st
Final Year Project
0
Prior Coding Experience

The Platform
What I built and how
A full-stack proptech platform built from scratch — data pipeline, machine learning model, AI integration, and professional frontend.
01
Data Research & Pipeline
Researched and compiled quarterly market data across 8 Central London submarkets from published reports by Savills, CBRE, Colliers, Knight Frank and BNP Paribas. Built a structured CSV pipeline feeding a Python backend.
Python Pandas Market Research
02
Machine Learning Forecasting
Trained a Linear Regression model using scikit-learn to forecast vacancy rates by submarket, using rent, take-up and historical vacancy as features. Model runs at startup and serves predictions via a REST API.
scikit-learn Linear Regression Feature Engineering
03
FastAPI Backend
Built a production REST API with FastAPI, deployed on Railway. Endpoints serve predictions, AI commentary, live web data, deal tracking and PDF generation. Handles CORS, environment variables and async requests.
FastAPI Railway REST API
04
Claude AI Integration
Integrated the Anthropic Claude API to generate real-time market commentary in the style of a Savills research note, and to search the web for the latest published market figures and deal transactions.
Anthropic API Web Search Prompt Engineering
05
Frontend & Product Design
Designed and built the entire frontend in vanilla HTML, CSS and JavaScript — including an interactive SVG submarket schematic, Chart.js data visualisations, animated KPI cards and a freemium access model with password gating.
HTML/CSS/JS Chart.js SVG Vercel
06
PDF Report Generation
Built an automated PDF report generator using ReportLab that produces a branded, institutional-quality research note on demand — including AI commentary, KPI tables, quarterly data breakdown and a disclaimer footer.
ReportLab PDF Generation Document Design

Skills Demonstrated
Technical & professional capability
Built through the project and underpinned by a real estate education.
🏢
Central London Office Markets
Submarkets, rental trends, vacancy dynamics, take-up analysis, agent research
🐍
Python Development
FastAPI, Pandas, scikit-learn, ReportLab, httpx, async programming
AI & Prompt Engineering
Claude API integration, web search tools, structured output, cost management
📊
Data Analysis & ML
Feature engineering, regression modelling, vacancy forecasting, data visualisation
🌐
Full Stack Deployment
Vercel (frontend), Railway (backend), Git, environment variables, CI/CD
📋
Real Estate Fundamentals
Valuation, development appraisal, property law, asset management, CoStar

Experience
Education & work history
2022 — 2025
University of Reading
Reading, UK
BSc Real Estate — 2:1
  • Modules included Real Estate Valuation, Applied Property Law, Development Appraisal and Finance, and Project in Valuation and Asset Management
  • Final year project received a First Class grade
  • Developed strong analytical and written skills through complex valuations, development appraisals and market analysis assignments
Aug 2024
Savills UK
Cardiff
Work Experience
  • Conducted research and analysis on an upcoming multi-million pound development project, supervised by senior colleagues
  • Attended a client meeting with the project developers, gaining insight into how major schemes are managed at institutional level
  • Shadowed professionals on site visits, observing client relationship management at a senior level
Oct 2025
Fletcher Morgan
Cardiff
Chartered Surveying Experience
  • Shadowed a company Director on multiple site visits with current and prospective clients
  • Assisted with administrative tasks and gained hands-on insight into the day-to-day responsibilities of a Chartered Surveyor
  • Developed understanding of client management and the practical application of surveying skills
Oct 2025
PMG Cardiff Ltd
Cardiff
Residential Property Insight
  • Met with company Directors of a specialist high-quality residential lettings business
  • Gained insight into the operational differences between founder-led firms and larger institutional property companies
  • Advanced sector knowledge through direct conversation with experienced practitioners
Get In Touch
Open to opportunities
I'm actively looking for graduate roles in commercial property, proptech, real estate data or related fields across London. If this platform has caught your attention, I'd love to talk.