The R scripts in this repository can be used to replicate the results in the paper

"Climate Policy and Transition Risk in the Housing Market"

Konstantinos Ferentinos & Alex Gibberd & Benjamin Guin

that has been published in the Bank of England Staff Working Paper Series: https://www.bankofengland.co.uk/working-paper/2021/climate-policy-and-transition-risk-in-the-housing-market.

Data Sources

There are five open data sources that need to be accessed in order to replicate the results of the paper.

1. Data on Property Transactions

The first open data source consists of Price Paid Data collected across England and Wales, that are sourced from the HM Land Registry website: https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads#yearly-file and https://data.gov.uk/dataset/314f77b3-e702-4545-8bcb-9ef8262ea0fd/archived-price-paid-data-1995-to-2017.

The specific CSV files that need to be downloaded from https://data.gov.uk/dataset/314f77b3-e702-4545-8bcb-9ef8262ea0fd/archived-price-paid-data-1995-to-2017 are:

Price Paid Data: 2015 part 1 — pp-2015-part1.csv
Price Paid Data: 2015 part 2 — pp-2015-part2.csv
Price Paid Data: 2016 part 1 — pp-2016-part1.csv
Price Paid Data: 2016 part 2 — pp-2016-part2.csv
Price Paid Data: 2017 part 1 — pp-2017-part1.csv
Price Paid Data: 2017 part 2 — pp-2017-part2.csv

The specific CSV files that need to be downloaded from https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads#yearly-file are:

part 1 of the 2018 yearly data as a CSV file (part 1) — pp-2018-part1.csv
part 2 of the 2018 yearly data as a CSV file (part 2) — pp-2018-part2.csv
the complete 2019 yearly data as a CSV file (complete) — pp-2019.csv

At the time of the analysis, I used the pp-2015.csv and pp-2016.csv complete yearly CSV files instead of the evenly split CSV files pp-2015-part1.csv, pp-2015-part2.csv, pp-2016-part1.csv, and pp-2016-part2.csv, that are mentioned above. If there are discrepancies in the data sourcing process due to updates to the CSV files from the HM Land Registry, please do not hesitate to contact me so as to share the specific CSV files I used in the analysis.

Contains HM Land Registry data © Crown copyright and database right 2020. This data is licensed under the Open Government Licence v3.0.

2. Data on Energy Performance Certificates (EPC)

The second open data source consists of the Energy Performance of Buildings Registers that are the official source for all EPCs issued for all domestic buildings and building units in England and Wales that have been constructed, sold or let since 2008, and are sourced from the Ministry of Housing, Communities & Local Government: https://epc.opendatacommunities.org/.

After signing up, an email will be sent with a link to access the data. The main download option is that of the full register, that consists of 340 data files, one for each local authority district to which the postcodes of the registered properties have been assigned, containing all the up to date EPC data from 2008 to the current date. Hence, the zip file that contains the full register needs to be downloaded and extracted to a folder named EPC.

3. Output Area Classification (December 2011) in the United Kingdom

The third open data source contains the Output Area Classifications (OACs) in their December 2011 format, that partition these areas into distinct groups, and are sourced from the Open Geography Portal: https://geoportal.statistics.gov.uk/datasets/be648a095a9745998f6961e5ba54e01c_0.

4. ONS Postcode Directory (May 2020)

The fourth open data source is the ONS Postcode Directory (ONSPD) for the United Kingdom in its May 2020 format, which relates both current and terminated UK postcodes to a range of current area geographies, among them the 2011 OAC codes, and is sourced from the Open Geography Portal: https://geoportal.statistics.gov.uk/datasets/ons-postcode-directory-may-2020, in the form of a zip file that should be downloaded and extracted to a folder named ONSPD.

The CSV files that are used in the R scripts are:

ONSPD_MAY_2020_UK.csv which can be found in the Data folder of the ONSPD folder
LAU218_LAU118_NUTS318_NUTS218_NUTS118_UK_LU.csv which can be found in the Documents folder of the ONSPD folder

5. NUTS Level 1 (January 2018) Full Clipped Boundaries in the United Kingdom

The fifth open data source contains the digital vector boundaries for Nomenclature of Territorial Units for Statistics (NUTS) Level 1, in the United Kingdom, as at January 2018, and are sourced from the Open Geography Portal: https://geoportal.statistics.gov.uk/datasets/01fd6b2d7600446d8af768005992f76a_0, in the form of the shapefile NUTS_Level_1__January_2018__Boundaries-shp.zip that should be downloaded and extracted to a folder named NUTS Level 1 shapefiles.

Description of R Scripts

Main Analysis Folder

The Main Analysis folder holds the R scripts required to replicate the results of the main analysis of the report.

data_upload.R contains the code that uploads the property transactions and EPC datasets
data_merging.R contains the code that sets up a panel dataset, combining price transactions with EPC data
data_processing.R contains the code that replicates the feature engineering process
descriptive_statistics.R contains the code that replicates the descriptive analyses
prices_map.R contains the code that constructs a heat map of property prices by regions
property_map.R contains the code that constructs a heat map of the number of properties by regions
propensity_score_matching.R contains the code that implements Propensity Score Matching to the pre-intervention treated and non-treated properties, in order to maximize their comparability
gam.R contains the code that presents a novel application of GAMs to assess the assumption that the outcomes in treatment and control group follow the same time trend in the absence of the intervention
did_analysis.R contains the code that implements the Difference-in-Difference regression model

Comparison of Samples Folder

The Comparison of Samples folder holds the R scripts required to replicate the comparison of a random sample of the population of all properties and the sample of repeated property sales employed in the main analysis.

sample_comp_data_upload.R contains the code that creates a random sample of the population of all properties
sample_comp_data_processing.R contains the code that pre-processes both the random sample of the population of all properties and the sample of repeated property sales employed in the main analysis
sample_comp_table.R contains the code that compares a random sample of the population of all properties and the sample of repeated property sales employed in the main analysis

Sensitivity Analysis Folder

The Sensitivity Analysis folder holds the R scripts required to replicate the results of the two sensitivity analyses of the report.

log_prices_analysis.R contains the code that replicates the sensitivity analysis considering a logarithmic transformation of the prices in the PSM matched dataset
5pc_analysis_data_processing.R contains the code that replicates the feature engineering process for the sensitivity analysis that trims at the 5th and 95th percentile of prices
5pc_analysis_psm.R contains the code that implements Propensity Score Matching to the pre-intervention treated and non-treated properties, in order to maximize their comparability, for the sensitivity analysis that trims at the 5th and 95th percentile of prices
5pc_analysis_gam.R contains the code that replicates the novel application of GAMs to assess the assumption that the outcomes in treatment and control group follow the same time trend in the absence of the intervention, for the sensitivity analysis that trims at the 5th and 95th percentile of prices
5pc_analysis_did.R contains the code that implements the Difference-in-Difference regression model for the sensitivity analysis that trims at the 5th and 95th percentile of prices

bttomio / BoE