House Prices and Rents: Micro Evidence from a Matched Dataset in Central London by Philippe Bracke

Download House Prices and Rents: Micro Evidence from a Matched Dataset in Central London by Philippe Bracke

Post on 27-Jan-2015

103 views

Category:

Technology

1 download

Embed Size (px)

DESCRIPTION

House Prices and Rents: Micro Evidence from a Matched Dataset in Central London by Philippe Bracke

TRANSCRIPT

<ul><li> 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) </li></ul> <p> 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. Todays Talk Roadmap 1. Introduction 2. Data 3. Matching procedure 4. Some ndings 5. More matching 6. Summary and way forward 4. (http://www.telegraph.co.uk/property/propertypicturegalleries/9054056/ The-best-Matt-cartoons-on-property.html) 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 dierences 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 + - 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, 19952013 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, 20062012 12. The problem: Data on private rents Rental data are much less available than house price data A gap exists in ocial private rental statistics with no ocial private rental index currently available The National Statisticians Review of Ocial Housing Market Statistics, September 2012 13. The problem: Data on private rents (contd) The Oce 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 Oce Agency (VOA), who deploys rental ocers to collect the price paid for privately rented properties This data is not publicly available 14. John D Wood &amp; Co. Rental Dataset Real estate agency with 14 London oces and 6 oces in the South-East of England Focus on upper market: Central/South-West London and countryside 15. John D Wood &amp; Co. (contd) Rental Dataset new contracts, no roll-overs internal records + exchange of data with other agencies 16. Weekly rent, Agency Dataset Central-Western London, 20062012 17. Matching procedure 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 &amp; 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-oor apartment 0.12 0.13 First-oor apartment 0.17 0.18 Second-oor apartment 0.17 0.15 Third-oor apartment 0.11 0.11 Fourth-oor+ apartment 0.12 0.16 Multi-level apartment 0.04 0.06 House 0.20 0.11 22. Descriptive stats (contd) Matched Units Complete Dataset Land Registry &amp; 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 oor area (sqft) 797 860 Furnished/unfurnished (%) Unfurnished 0.25 0.24 Partly furnished 0.34 0.27 Furnished 0.41 0.49 23. Some ndings 24. Matched dataset Rent-price ratio over time .02.04.06.08 01jul2006 01jan2008 01jul2009 01jan2011 01jul2012 R/P ratio 10year 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) Rentprice ratios vs Prices 0.02.04.06.08.1 0 500 1000 1500 2000 2500 Rent (in per week) Rentprice 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) Rentprice ratios vs Prices (Apartm.) 0.02.04.06.08.1 0 1000 2000 3000 4000 Price (in 1,000) Rentprice ratios vs Prices (Houses) .02.04.06.08.1 0 1000 2000 3000 4000 Floor area (sqft) Rentprice 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) Rentprice ratios vs Prices (by Postcode) Patterns conrmed by multivariate regression: Rent Price = + Type 1 + Size 2 + Location 3 + Date 4 + 27. Depreciation/maintenance costs and rent-price ratios Rent Price = rf + 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 + Eg + m Need to nd 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 eect 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 Eect of size Eect of location and other eects 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 Lets collaborate! Github, philippebracke Thank you! </p>