Bangladesh
Technical Assistance Report-Residential Property Price Indices Mission
Author: International Monetary Fund. Statistics Dept.
Publication Date:
10 Jun 2020
eISBN:
9781513546902
ISBN:
9781513546902
Language:
English
Keywords:
ISCR; CR; RPPI Guide; BB staff; residential property price index; BB data; RPPI team member; BB governor; RPPI guide; Data collection; Housing; quarterly RPPI; RPPI team member Research Department; compilation of a residential property price index; RPPI time
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The contents of this report constitute technical advice provided by the staff of the IMF to the authorities of Bangladesh in response to their request for technical assistance. The purpose of the mission was to assist the Bangladesh Bank (BB) in progressing on the compilation of a residential property price index. BB has plans to set up a new data collection system to improve the current existing data starting from July 2020. The new data collection will expand the geographic coverage and the type of dwellings and mostly will increase the current sample resulting in more accurate results. The mission recommended the use of R instead of other software since it allows to perform all the necessary calculations in one script and single software. The mission provided training particularly on the hedonic methods, chain linking and rebasing. The hedonic methods are the most recommended to address the quality changes on the mix of dwellings transacted when following the price of real estate.

Abstract

The contents of this report constitute technical advice provided by the staff of the IMF to the authorities of Bangladesh in response to their request for technical assistance. The purpose of the mission was to assist the Bangladesh Bank (BB) in progressing on the compilation of a residential property price index. BB has plans to set up a new data collection system to improve the current existing data starting from July 2020. The new data collection will expand the geographic coverage and the type of dwellings and mostly will increase the current sample resulting in more accurate results. The mission recommended the use of R instead of other software since it allows to perform all the necessary calculations in one script and single software. The mission provided training particularly on the hedonic methods, chain linking and rebasing. The hedonic methods are the most recommended to address the quality changes on the mix of dwellings transacted when following the price of real estate.

Summary of Mission Outcomes and Priority Recommendations

1. The purpose of the mission was to assist the Bangladesh Bank (BB) in progressing on the compilation of a residential property price index (RPPI). This will be the first technical assistance (TA) mission to Bangladesh on the RPPI to be conducted under the auspices of the Data for Decisions Fund (D4D). The aim of the mission is to assist the BB in improving data for RPPI compilation and to compile an experimental RPPI.

2. The Second Phase of the G-20 Data Gaps Initiative and guidance on Financial Soundness Indicators identify RPPI as a critical ingredient of financial stability policy analysis and macroprudential measures. In addition, an RPPI is on its own a macro-economic indicator of growth and a key indicator for understanding financial market conditions. A reliable RPPI is essential for informed economic policy making. The compilation of the RPPI will facilitate the BB in its assessment of developments and risks in property markets and increase the understanding of the link between property asset prices and financial assets. Currently, Bangladesh lacks a reliable official measure of trends in residential property prices.

3. BB is aiming at compiling a quarterly RPPI covering new flats for the capital city, Dhaka. The mission recommended to obtain all data including the secondary market and rest of the country to be stored for future expansion of the index coverage.

4. BB will begin the compilation of the RPPI with the time dummy method with a rolling window of four quarters. The index should be compiled with data from 2007 onwards. The mission adapted the R scripts of the RPPI Practical Compilation Guide (RPPI Guide) to the BB data. The RPPI Guide can be found at https://www.imf.org/en/Data/Statistics/RPPI-guide.

5. BB has plans to set up a new data collection system to improve the current existing data starting from July 2020. The new data collection will expand the geographic coverage and the type of dwellings and mostly will increase the current sample resulting in more accurate results.

Table 1.

Priority Recommendations

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Further details on the priority recommendations and the related actions/milestones can be found in the action plan under Detailed Technical Assessment and Recommendations.

Detailed Technical Assessment and Recommendations

A. Governance

6. The BB staff has the required skills, namely on R programming, to compile an RPPI. However, assistance is required on specific methodological issues. Currently, a team of nine staff members from different departments in the BB is compiling the index. Three staff members attended the IMF courses on 2017 and 2019 on RPPI and the lessons learned were implemented to improve the experimental RPPI, for example the use of the size variable as continuous.

7. The mission recommended the use of R instead of other software since it allows to perform all the necessary calculations in one script and single software. The RPPI requires econometric regressions and specific price index calculations. With R all can be made on the same script. Therefore at least six members of the team should have some knowledge in R.

8. BB is aiming at compiling a quarterly RPPI covering new flats for the capital city, Dhaka. There are data for other cities and other types of property but is not sufficient for a robust indicator, therefore for the moment only data on new flats will be used. The mission recommended to continue obtaining all data including the secondary market and rest of the country to be stored for future expansion of the index coverage.

9. The base year of the RPPI should be the same as the Consumer Price Index (CPI) and the Gross Domestic Product (GDP) to facilitate the benchmarking. BBS is aiming at rebasing both, the CPI and the GDP, to the fiscal year that runs from July 2015–June 2016 during 2020.

B. Compilation with the Current Dataset

10. Currently BB receives quarterly data from Delta Brac Housing Finance Corporation, ltd (DBH) on house loans. DBH is a financial institution specialized on housing loans. The DBH dataset contains the valuation price, the size of the dwelling and the location within the city. Data is available since 1998 with over 33,000 observations in total and the work on the index began in 2017. Experimental indices have been compiled by the BB staff with different methods as can be seen in the following figure.

Figure 1.
Figure 1.

RPPI Compiled by the BB

Citation: IMF Staff Country Reports 2020, 195; 10.5089/9781513546902.002.A001

11. The 21 locations should be clustered in order to have at least around 50 observations per quarter in each location cluster. As shown in the table below the number of observations per location is in many cases much lower than 50, in result the coefficient of the correspondent dummy variable may have a very low significance. The clustering should be made based on some economic criteria as for example income level.

Table 2.

Number of Observations per Location for the Period 1Q2017

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12. The mission provided training particularly on the hedonic methods, chain linking and rebasing. The hedonic methods are the most recommended to address the quality changes on the mix of dwellings transacted when following the price of real estate. The data currently available has few variables but are enough to compile an index with a hedonic method and that is preferred to other methods where the quality mix is not treated at all. The BB staff had the need on TA regarding price indices calculations namely on rebasing and chain linking the indices. The mission made some practical exercises with the BB data and examples can be seen in Table 3 and in Figure 2.

Table 3.

Chained and Unchained Indexes

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Figure 2.
Figure 2.

Chained and Unchained Index

Citation: IMF Staff Country Reports 2020, 195; 10.5089/9781513546902.002.A001

13. The mission recommended the compilation of the RPPI starting from 2007 with the time dummy method with a rolling window of four quarters. The figure below shows the number of observations per year. Each quarter has less than 1,000 observations. Considering the small sample available, to obtain a more robust and less volatile indicator, the time dummy is more appropriate. In addition, data are available since 1998 but until 2007 there are few observations. The index should be compiled with data from 2007 onwards. The mission adapted the R scripts of the RPPI Guide to the BB data.

Figure 3.
Figure 3.

Number of Observations per Year

Citation: IMF Staff Country Reports 2020, 195; 10.5089/9781513546902.002.A001

*2019 only two quarters

14. The mission provided guidance on how to analyze and treat the available data. A R script was created for the BB data to perform basic data manipulations namely treating the outliers, missing values and duplicates. Further guidance can be found on the RPPI Guide on how to analyze and prepare data for processing. Some results can be seen in the following table and histograms.

Table 4.

Descriptive Statistics for the 1Q2017 Before Data Treatment

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Figure 4.
Figure 4.

Histograms Before and After Data Treatment—1Q 2017

Citation: IMF Staff Country Reports 2020, 195; 10.5089/9781513546902.002.A001

15. The mission recompiled the index with BB data from 2007 with the base year July 2015/June 2016. The R script for the time dummy hedonic method calculation prepared for the RPPI Guide was adapted for the BB data and the staff was trained to its manipulation. The results are in the figure below:

Figure 5.
Figure 5.

RPPI Time Dummy with Rolling Window

Citation: IMF Staff Country Reports 2020, 195; 10.5089/9781513546902.002.A001

C. Improving the Data Collection

16. BB staff prepared a template for the survey that was reviewed by the mission and presented to the potential data providers. In general, the mission recommends keeping it as short as possible to avoid respondent burden. A long questionnaire can incur many missing values. The template should not have any open questions. The questions should be objective and mensurable with expected answers to be yes/no or numbers. The use of semantic is highly prone to typing errors and lack of normalization and thus not machine readable.

17. The data structure shown in the table below was built from the feedback obtained during the meeting with the data providers. The data structure will be sent to the data providers for their final assessment and for incorporation in their system.

Table 5.

Data Structure

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18. BB should send an official data request to the data providers. The data providers mentioned the need of an official request for their internal procedures. This request should be sent with the final data structure and no later than March to allow time for the data providers to adapt their systems.

19. The data will be transmitted and stored using the Enterprise Data Warehouse (EDW) from July 2020. Data providers need some time to adapt their internal systems for the new data collection. After the adjustment period the EDW that is the current system for the automated data transmission by the financial institutions will be used to the real estate data.

D. Dissemination

20. BB may explore the possibility to begin the publication of the RPPI with the new data from July 2021. In the meantime, the regular compilation will continue for the use of internal use. The mission provided recommendations for the dissemination as exemplified on the RPPI Guide. In addition, a draft technical note was provided as in Appendix I.

To support progress in the above work areas, the mission recommended a detailed action plan with the following priority recommendations carrying particular weight to make headway in improving the RPPI:

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E. Officials Met During the Mission

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Appendix I. RPPI Technical Note

Residential Property Price Index (RPPI) Technical Note

1. Background

The residential property price index (RPPI) measures the price evolution for residential new apartments in Dhaka. Reliable property price indexes are essential for the Bangladesh Bank (BB) to assess developments and risks in the real estate market and to understand the linkages between residential real estate markets and financial soundness.

2. Coverage

The coverage of the RPPI is limited to Dhaka city and covers the primary (new dwellings) market for apartments.

3. Data Sources

The BB collects data from Delta Brac Housing Finance Corporation, ltd (DBH) on loans for new apartments.

4. Periodicity

The RPPI is disseminated on a quarterly basis.

5. Base Period

The RPPI is an annually chain-linked price index that uses the year 2015/106 as reference year. The average of the quarterly indexes for 2015/16 equals 100.

6. Dissemination

The RPPI is released on a quarterly basis, 45 days after the end of the reference period.

7. Methodology – Conceptually

To compile an RPPI, key characteristics of the dwellings are required to assure a constant quality index. As with any price index the target is to follow the price trend by comparing like-with-like. For property price indexes this is particularly challenging since a direct price comparison requires comparable dwellings or the same dwelling to be available in consecutive periods. This is generally not the case with residential properties where the same residential property is only sold every couple of decades. Given the infrequent sale and the heterogeneity of residential properties, quality adjustment techniques are required to derive measures of pure price change. This means that the data requirements for a high quality RPPI are extensive and rely heavily on detailed characteristics about each property given there are a wide range of characteristics that can influence the price of a dwelling.

8. Methodology -Time Dummy Hedonic Method

The time dummy method measures the effect of “time” on the price (p). Prices of all dwellings (n) for several periods (t), are pooled in the same regression for every stratum, on their characteristics (znkt) and on dummy variables for the periods (Dnt). The main advantage of the method is its simplicity, since the index follows directly from the estimated time dummy parameters.

It is generally appropriate:

  • with few transactions

  • or aiming at compiling a monthly index

  • or more detailed stratification.

A log-linear specification for each stratum is applied:

ln⁡pnt=β0+Σt=1TδtDnt+Σk=1Kβkznkt+ϵnt

The index for each stratum for a period t is derived by exponentiation of the time dummy parameters δ^t:

It=exp⁡(δ^t)*100

For the reference period a dummy variable is not included, the price index for the price reference period is set to equal 100.

When a new period is added to the data and the model is re-estimated, the indexes from the previous periods will most likely change since the estimated parameters δ^t of older periods will differ from their previous estimate. Therefore, a rolling window approach would need to be applied.

9. Chaining

To avoid revisions to the indices a rolling window approach is used. Normally for quarterly indices the “shadow” prices of the characteristics (β^) are kept fixed for at least a year. This means that data from 12 months are used together. Every quarter a regression is estimated with data from the current quarter and the previous three quarters. The indices from the “new window” and the “previous window” are chained by using the last overlap period between the two windows.

An example is given in the table below for a quarterly index:

Table 1.

Example of Chaining the Indexes from Rolling Window Hedonic Regression

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Same Series

Other IMF Content

Other Publishers

Cover IMF Staff Country Reports
Bangladesh: Technical Assistance Report-Residential Property Price Indices Mission
Author: International Monetary Fund. Statistics Dept.
Volume/Issue:
Volume 2020: Issue 195
Publisher:
International Monetary Fund
ISBN:
9781513546902
ISSN:
1934-7685
Pages:
17
DOI:
https://doi.org/10.5089/9781513546902.002

RPPI Compiled by the BB

Chained and Unchained Index

Number of Observations per Year

Histograms Before and After Data Treatment—1Q 2017

RPPI Time Dummy with Rolling Window