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

House Prices and RentsMicro Evidence from a Matched Dataset in Central London

Philippe Bracke

London School of Economics

PyData 2014, London (Feb 23)

About me

I Studied economics

I Wanted to become a theoreticalmacroeconomist

I PhD: discovered the joys of data analysisI Python (and R, Stata)

Current research focus Housing markets

Twitter @PhilippeBracke

Today’s TalkRoadmap

1. Introduction

2. Data

3. Matching procedure

4. Some findings

5. More matching

6. Summary and way forward

(http://www.telegraph.co.uk/property/propertypicturegalleries/9054056/The-best-Matt-cartoons-on-property.html)

Focus of this research

Price

Rentor

Rent

Price(Rental yield)

They matter hugely for...

Households Buy vs rent

Landlords Return on investment

Aggregate ratio between house prices and rents:important indicator of housing market conditions

Micro-level differences in rental yields: equally important

Why does RentPrice change?

(over time and over space)

Rent = User cost ·Price (“no-arbitrage”)

Rent

Price= rf + δ - Eg + m

I Interest rate

I Risk

I Expected growth

I Maintenance

Data

1. House prices

2. (Private-sector) rents

Land Registry Price Paid data

All registered property sales in England and Wales, 1995–2013

→ 18.5m records, freely available!

I full address

I price paid

I date of transfer

I property type: Detached, Semi, Terraced or Flat/Maisonette

I new build or not

I freehold or leasehold

http://www.landregistry.gov.uk/market-trend-data/public-data/

price-paid-data

Transaction prices in London, 2006–2012

The problem: Data on private rents

I Rental data are much less available than house price data

A gap exists in official private rental statistics with noofficial private rental index currently available

The National Statistician’s Review of Official Housing MarketStatistics, September 2012

The problem: Data on private rents (cont’d)

I The Office for National Statistics (ONS) released in 2013 anexperimental quarterly index of the private rental market

I The index is based on individual rental data from theValuation Office Agency (VOA), who deploys rental officers tocollect the price paid for privately rented properties

I This data is not publicly available

John D Wood & Co.Rental Dataset

I Real estate agency with 14 London offices and 6 offices in theSouth-East of England

I Focus on upper market: Central/South-West London andcountryside

John D Wood & Co. (cont’d)Rental Dataset

I new contracts, noroll-overs

I internal records +exchange of data withother agencies

Weekly rent, Agency DatasetCentral-Western London, 2006–2012

Matching procedure

Matching issuesAddress 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

Matched datasetConstruction

I try as much as possible to harmonise the two datasetsI all variables in upper case letters as in LRI rename “hseno” as “paon”, and “hsname” as “saon”

I 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

Matched datasetDistance between sale and rental contract

050

010

0015

0020

00M

atch

es

−2000 −1000 0 1000 2000Days

Descriptive stats

Matched Units Complete DatasetLand Registry & Rentals Rentals

Observations 1,922 48,341

Median rent 595 525Median price 650,000Median gross rent-price ratio 0.05

Property type (%)Lower-ground apartment 0.07 0.08Ground-floor apartment 0.12 0.13First-floor apartment 0.17 0.18Second-floor apartment 0.17 0.15Third-floor apartment 0.11 0.11Fourth-floor+ apartment 0.12 0.16Multi-level apartment 0.04 0.06House 0.20 0.11

Descriptive stats (cont’d)

Matched Units Complete DatasetLand Registry & Rentals Rentals

Bedrooms (%)1-bedroom property 0.33 0.362-bedroom property 0.41 0.413-bedroom property 0.16 0.154-bedroom+ property 0.10 0.07

Apartment block 0.16 0.31

Median floor area (sqft) 797 860

Furnished/unfurnished (%)Unfurnished 0.25 0.24Partly furnished 0.34 0.27Furnished 0.41 0.49

Some findings

Matched datasetRent-price ratio over time

.02

.04

.06

.08

01jul2006 01jan2008 01jul2009 01jan2011 01jul2012

R/P ratio 10−year UK Government Bond Yield

Matched datasetRent-price ratio vs. property value

0.0

2.0

4.0

6.0

8.1

0 1000 2000 3000 4000Price (in £1,000)

Rent−price ratios vs Prices

0.0

2.0

4.0

6.0

8.1

0 500 1000 1500 2000 2500Rent (in £ per week)

Rent−price ratios vs Rents

Matched datasetRent-price ratio vs. property type

.02

.04

.06

.08

.1

0 1000 2000 3000 4000Price (in £1,000)

Rent−price ratios vs Prices (Apartm.)

0.0

2.0

4.0

6.0

8.1

0 1000 2000 3000 4000Price (in £1,000)

Rent−price ratios vs Prices (Houses)

.02

.04

.06

.08

.1

0 1000 2000 3000 4000Floor area (sqft)

Rent−price ratios vs Floor areas

NW1

NW3

NW8

SW1

SW10

SW11

SW3

SW5

SW6 SW7

SW8

W1W10

W11W14W2

W8

W9

.046

.048

.05

.052

.054

.056

400 600 800 1000 1200Average 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

Depreciation/maintenance costs and rent-price ratiosRent

Price= rf + δ − g + m

House = land + structure

I More expensive locations: higher land share ⇒ RentPrice ↓

More MatchingRepeat sales, repeat rentals

How to measure future appreciation and risk?Rent

Price= rf + δ − Eg + m

I Need to find future sales and/or rentals of the same property

→ Match within-Land Registry or within-Agency dataI easier

Repeat sales: not frequent

Repeat rentals: many

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

I One-standard deviation higher future rent appreciation⇒ Rent

Price ↓ by 1.6%

I Ambiguous results on rent volatility (one measure of risk)

Summary and way forward

Summary

I Novel dataset on prices and rents in Central LondonI Measure rent-price ratios directly for matched properties

I Find lower rent-price ratios for expensive properties

→ Effect of size→ Effect of location

and other effects

I Consistent with economic theory

Next steps

I The Land Registry is a recent open data resource with hugepotential

I Can be matched with many other datasetsI private datasetsI public housing-related websites

I Let’s collaborate!I Github, philippebracke

Thank you!