House Prices and Rents: Micro Evidence from a Matched Dataset in Central London by Philippe Bracke
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This document summarizes a presentation about analyzing relationships between house prices and rents in central London using a novel matched dataset. Key points: 1. The presenter created a matched dataset linking publicly available house price data to a private rental agency's records, allowing direct comparison of prices and rents for the same properties. 2. Preliminary findings show rent-to-price ratios are lower for more expensive properties, likely due to land values, and differ by property type and location as economic theory predicts. 3. Next steps involve matching repeated sales and rentals of the same properties to better measure future home price appreciation and risk, which can further explain rent-to-price ratio differences. The presenter invites collaboration
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House Prices and Rents: Micro Evidence from a Matched Dataset in Central London by Philippe Bracke
- 1. House Prices and Rents Micro Evidence from a Matched Dataset in Central London Philippe Bracke London School of Economics PyData 2014, London (Feb 23)
- 2. About me Studied economics Wanted to become a theoretical macroeconomist PhD: discovered the joys of data analysis Python (and R, Stata) Current research focus Housing markets Twitter @PhilippeBracke
- 3. Today’s Talk Roadmap 1. Introduction 2. Data 3. Matching procedure 4. Some findings 5. More matching 6. Summary and way forward
- 5. Focus of this research Price Rent or Rent Price (Rental yield) They matter hugely for... Households Buy vs rent Landlords Return on investment
- 6. Aggregate ratio between house prices and rents: important indicator of housing market conditions
- 7. Micro-level differences in rental yields: equally important
- 8. Why does Rent Price change? (over time and over space) Rent = User cost · Price (“no-arbitrage”) Rent Price = rf + δ - Eg + m Interest rate Risk Expected growth Maintenance
- 9. Data 1. House prices 2. (Private-sector) rents
- 10. Land Registry Price Paid data All registered property sales in England and Wales, 1995–2013 → 18.5m records, freely available! full address price paid date of transfer property type: Detached, Semi, Terraced or Flat/Maisonette new build or not freehold or leasehold http://www.landregistry.gov.uk/market-trend-data/public-data/ price-paid-data
- 11. Transaction prices in London, 2006–2012
- 12. The problem: Data on private rents Rental data are much less available than house price data A gap exists in official private rental statistics with no official private rental index currently available The National Statistician’s Review of Official Housing Market Statistics, September 2012
- 13. The problem: Data on private rents (cont’d) The Office for National Statistics (ONS) released in 2013 an experimental quarterly index of the private rental market The index is based on individual rental data from the Valuation Office Agency (VOA), who deploys rental officers to collect the price paid for privately rented properties This data is not publicly available
- 14. John D Wood & Co. Rental Dataset Real estate agency with 14 London offices and 6 offices in the South-East of England Focus on upper market: Central/South-West London and countryside
- 15. John D Wood & Co. (cont’d) Rental Dataset new contracts, no roll-overs internal records + exchange of data with other agencies
- 16. Weekly rent, Agency Dataset Central-Western London, 2006–2012
- 18. Matching issues Address format Land Registry Clean and easy: postcode W2 3DB paon 5 saon FLAT K street WESTBOURNE CRESCENT Ambiguous: postcode UB4 8FJ paon MARSH COURT, 561 saon 4 street UXBRIDGE ROAD Agency data Clean and easy: hsename Flat K hseno 5 address1 Westbourne Crescent postcode W2 Ambiguous: hsename hseno 2 address1 Rupert House address2 Nevern Square
- 19. Matched dataset Construction try as much as possible to harmonise the two datasets all variables in upper case letters as in LR rename “hseno” as “paon”, and “hsname” as “saon” join together all transactions sharing the same “street”, “paon” and “saon” Rule 1 for each sale, keep the closest rent Rule 2 for each rent, keep the closest sale
- 20. Matched dataset Distance between sale and rental contract 0500100015002000 Matches −2000 −1000 0 1000 2000 Days
- 21. Descriptive stats Matched Units Complete Dataset Land Registry & Rentals Rentals Observations 1,922 48,341 Median rent 595 525 Median price 650,000 Median gross rent-price ratio 0.05 Property type (%) Lower-ground apartment 0.07 0.08 Ground-floor apartment 0.12 0.13 First-floor apartment 0.17 0.18 Second-floor apartment 0.17 0.15 Third-floor apartment 0.11 0.11 Fourth-floor+ apartment 0.12 0.16 Multi-level apartment 0.04 0.06 House 0.20 0.11
- 22. Descriptive stats (cont’d) Matched Units Complete Dataset Land Registry & Rentals Rentals Bedrooms (%) 1-bedroom property 0.33 0.36 2-bedroom property 0.41 0.41 3-bedroom property 0.16 0.15 4-bedroom+ property 0.10 0.07 Apartment block 0.16 0.31 Median floor area (sqft) 797 860 Furnished/unfurnished (%) Unfurnished 0.25 0.24 Partly furnished 0.34 0.27 Furnished 0.41 0.49
- 23. Some findings
- 24. Matched dataset Rent-price ratio over time .02.04.06.08 01jul2006 01jan2008 01jul2009 01jan2011 01jul2012 R/P ratio 10−year UK Government Bond Yield
- 25. Matched dataset Rent-price ratio vs. property value 0.02.04.06.08.1 0 1000 2000 3000 4000 Price (in £1,000) Rent−price ratios vs Prices 0.02.04.06.08.1 0 500 1000 1500 2000 2500 Rent (in £ per week) Rent−price ratios vs Rents
- 26. Matched dataset Rent-price ratio vs. property type .02.04.06.08.1 0 1000 2000 3000 4000 Price (in £1,000) Rent−price ratios vs Prices (Apartm.) 0.02.04.06.08.1 0 1000 2000 3000 4000 Price (in £1,000) Rent−price ratios vs Prices (Houses) .02.04.06.08.1 0 1000 2000 3000 4000 Floor area (sqft) Rent−price ratios vs Floor areas NW1 NW3 NW8 SW1 SW10 SW11 SW3 SW5 SW6 SW7 SW8 W1W10 W11W14 W2 W8 W9 .046.048.05.052.054.056 400 600 800 1000 1200 Average Price (in £1,000) Rent−price ratios vs Prices (by Postcode) Patterns confirmed by multivariate regression: Rent Price = α + Type β1 + Size β2 + Location β3 + Date β4 + ε
- 27. Depreciation/maintenance costs and rent-price ratios Rent Price = rf + δ − g + m House = land + structure More expensive locations: higher land share ⇒ Rent Price ↓
- 28. More Matching Repeat sales, repeat rentals
- 29. How to measure future appreciation and risk? Rent Price = rf + δ − Eg + m Need to find future sales and/or rentals of the same property → Match within-Land Registry or within-Agency data easier Repeat sales: not frequent Repeat rentals: many
- 30. The effect of future appreciation and risk Sales Rentals Matched Dataset Matched + Repeat Rentals Dataset 1,922 properties 859 properties Max gap = 180 days Average gap = 85 days Max gap = 2,360 days Average gap = 578 days Regression results One-standard deviation higher future rent appreciation ⇒ Rent Price ↓ by 1.6% Ambiguous results on rent volatility (one measure of risk)
- 31. Summary and way forward
- 32. Summary Novel dataset on prices and rents in Central London Measure rent-price ratios directly for matched properties Find lower rent-price ratios for expensive properties → Effect of size → Effect of location and other effects Consistent with economic theory
- 33. Next steps The Land Registry is a recent open data resource with huge potential Can be matched with many other datasets private datasets public housing-related websites Let’s collaborate! Github, philippebracke Thank you!