## INDONESIA — REPORT ON RESIDENTIAL PROPERTY PRICE STATISTICS CAPACITY DEVELOPMENT MISSION (FEBRUARY 7–9, 2018) — Section 1

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---

### SUMMARY OF MISSION OUTCOMES AND PRIORITY RECOMMENDATIONS
- Mission: Technical Assistance (TA) mission conducted February 7–9, 2018 to assist Bank Indonesia (BI) with development of the residential property price index (RPPI); advice also provided on the commercial property price index (CPPI).
- Current BI practice:
  - BI compiles and disseminates an RPPI based on quarterly surveys of major developers covering the primary (new) market for single-unit dwellings in 16 cities (with an additional two cities being added).
  - BI currently collects secondary market series based on appraised values from sellers and real estate agents in 10 major cities (series not yet published).
  - BI publishes CPPI statistics based on appraisal data for 7 segments in 10 major cities (11th being added).
- Key findings and guidance:
  - Redevelop weighting of component indexes to the national total, building on 2016 survey improvements that included more property characteristic detail.
  - Short-term: assess potential for using mortgage data to compile weights, using aggregated value of mortgage drawdowns by region and property type (mortgage data used as weighting observations, not price observations).
  - Longer-term: assess potential for using taxation data to compile weights based on recorded value of residential property transactions by region and property type; develop a strategy for accessing taxation data given Indonesia’s decentralized taxation system.
  - Continue development of an alternative RPPI using Big Data (listings) from real estate web portals to compile robust asking-price indexes for the secondary market using a hedonic methodology.
  - Continue survey-based approach for the primary market; survey approach follows a matched-model methodology so hedonic “in-strata” property mix-adjustment is unnecessary for the primary market.
  - For CPPI, the headline index is an index of activity (incorporates market capitalization rates and rent prices) rather than a pure sales-price index; BI should expand published CPPI statistics to include a national sales price index and a rent price index.
  - Exclude apartments data from future CPPI and include apartments in the RPPI instead.
- Priority recommendations (Table 1):
  - Target Date August 2018 — Develop a strategy for accessing taxation data on property transfers. — Responsible: BI
  - Target Date September 2018 — Assess the potential for using mortgage data to support implementation of improved weighting for property price indexes for the primary market. — Responsible: BI
  - Target Date December 2018 — Compile prototype hedonic RPPIs using listings data for the secondary market in the capital city (Jakarta). — Responsible: BI
  - Target Date May 2019 — Extend the current CPPI publication to include aggregate pure sales price and rent price indexes. — Responsible: BI

### DETAILED TECHNICAL ASSESSMENT AND RECOMMENDATIONS
- Outcome: Improved price statistics for Indonesia

A. Action Plan — Key milestones and targets
- Priority actions and target completion dates:
  - Develop a strategy to secure access to taxation data on property transfers. — Target Completion Date: August 2018
  - Assess the potential for using mortgage data to support the implementation of improved weighting for property price indexes. — Target Completion Date: September 2018
  - Subset residential listings data to include only first instances of each listing. — Target Completion Date: April 2018
  - Develop prototype hedonic RPPI for Jakarta. — Target Completion Date: December 2018
  - Extend prototype to other cities (3 cities, Surabaya, Medan, and Makassar). — Target Completion Date: December 2019
  - Prepare strategy for further development of RPPI. — Target Completion Date: March 2020
  - Extend current CPPI publication to include aggregate pure sales price and rent price indexes. — Target Completion Date: May 2019

B. Introduction — Context
- TA mission follows a diagnostic mission to BI during October 14–16, 2014.
- BI staff from the Real Sector Statistics Division and the Big Data team actively participated.

C. Residential Property Price Index (RPPI) — Technical assessment and recommendations
- Current compilation:
  - RPPI series compiled from quarterly face-to-face surveys of major developers; includes prices, number of units built and sold, expectations, and qualitative data.
  - RPPI covers primary (new) single-unit dwellings in 16 cities (plus two being added).
- Methodology:
  - Survey-based approach implicitly follows a matched-model methodology; no hedonic “in-strata” property mix-adjustment necessary.
  - Current stratification design and index compilation formula assessed as reasonable.
- Data limitations and opportunities:
  - Administrative data (taxation records and mortgage data) currently unsuited as price observations because taxation records lack sufficient property characteristic detail and taxation is decentralized to 34 municipal agencies (non-standardized collection, processing, storage).
  - Short-term use of mortgage data recommended for weighting (aggregated mortgage drawdown values by region and property type).
  - Long-term exploration of taxation data for weighting recommended; strategy should assess feasibility given decentralization and potential need for harmonized administration system. Enhanced taxation records could include limited additional characteristics (property type and vintage: primary, secondary).
- Recommended actions (RPPI):
  - Assess the potential for using mortgage data to compile weights, based on aggregated value of mortgage drawdowns in different regions and for different property types.
  - Develop a strategy for securing access to taxation data on property transfers.

D. The Big Data Project — Use of listings for secondary market RPPI
- Current status:
  - BI receives listings data monthly from three real estate web portals covering 50 cities. Data fields include offer type (rent or sale), asking price, property type, lot size, dwelling size, number of bedrooms, number of bathrooms, address, and free-text characteristics (garage, gated property, swimming pool).
  - Work to date has focused on data preparation, extraction, and prototype indexes using simple mix-adjustment by stratification.
- Recommended focus and methodology:
  - Continue the Big Data project to develop robust asking-price indexes for the secondary market using hedonic methods.
  - Listings data primarily cover the secondary market; continue survey approach for the primary market.
  - Subset listings to include only first instances of each listing at a unique price: include a listing only in the first month it appears, and include updates only in the month of price revision; do not include the same listing price for more than one month.
  - Distinguish rental listings from sale offers; distinguish apartment listings from landed houses.
- Prototype hedonic RPPI for Jakarta — recommended target: by end of 2018
  - Recommended single methodology for initial learning: semi-log rolling-window time-dummy method.
  - Detailed compilation steps:
    - Subset listings to first-instance sales listings for each property at a unique price.
    - Focus on Jakarta and use semi-log rolling window time-dummy method.
    - Initially use core property characteristics only; consider free-text later.
    - Compile and analyze summary statistics for prices and characteristics to inform edit checks and thresholds.
    - Develop appropriate locational stratification balancing granularity with sufficient observations per stratum.
    - Run regressions for 12-month windows and check consistency of diagnostics across windows.
    - Use dummy variables for categorical values; test grouping continuous values as dummies (example: age grouped as new/1-5 years/6-11 years).
    - Apply outlier identification tests (Cook’s distance recommended); remove outliers and rerun regressions.
    - Check plausibility and significance of coefficients and overall model fit/diagnostics.
    - Weight component indexes by value of advertised properties.
- Recommended action (Big Data):
  - Continue the Big Data project, guided by the detailed steps outlined above.

E. The Commercial Property Price Index (CPPI) — Technical assessment and recommendations
- Current compilation:
  - CPPI based on appraisal data from external property market experts; covers 7 segments (offices, retail, apartments, hotels, industrial estates, convention halls, and warehouses) in 10 major cities (11th being added).
  - Data collected via face-to-face interviews with more than 2,700 respondents covering supply, occupancy, sales, and rents.
  - Rent-price and sales-price indexes compiled for segments in each region and weighted to a national index using market capitalization values.
  - Headline CPPI is an index of activity (incorporates market capitalization rates and rent prices), not a pure sales-price index.
- Recommendations:
  - Extend published CPPI statistics to include a national sales price index and a rent price index.
  - Target for publication of expanded statistical release: Quarter 1, 2019 (authorities and mission agreed on this target).
  - Exclude apartments from future CPPI and include apartments in the RPPI.
- Recommended action (CPPI):
  - Extend the current CPPI publication to include a national sale price index and a rent price index.

F. Officials Met During the Mission — Selected contacts
- Ms. Listyowati Puji Lestari — Real Sector Statistics Division, Statistics Department, Bank Indonesia — RPPI — listyowati_p@bi.go.id
- Ms. Herina Prasnawaty D — Real Sector Statistics Division, Statistics Department, Bank Indonesia — RPPI/CPPI — herinapd@bi.go.id
- Ms. Widi Agustin S. (contact details truncated in source)

### INSTITUTIONAL UNIT AND PRIMARY CONTACTS (Section 2)
- Institutional Unit:
  - Real Sector Statistics Division
  - Statistics Department
  - Bank Indonesia
  - RPPI/CPPI
- Primary Contacts:
  - Mr. Irfan Sampe — Big Data Unit, Statistics Department, Bank Indonesia — irfans@bi.go.id
  - Mr. Alvin Andhika Zulen — Big Data Unit, Statistics Department, Bank Indonesia — alvin_az@bi.go.id
  - Ms. Kumala Kristiawardani — Big Data Unit, Statistics Department, Bank Indonesia — kumala@bi.go.id
  - Ms. Hidayah Dhini Ari — Big Data Unit, Statistics Department, Bank Indonesia — dhini_ari@bi.go.id
  - Ms. Stephanie Gunawan — Real Sector Statistics Division, Statistics Department, Bank Indonesia — stephanie_g@bi.go.id
- Document Identifiers and Affiliations:
  - INDONESIA
  - INTERNATIONAL MONETARY FUND    11
  - RPPI/CPPI
  - CPPI
  - Contact email: widi_as@bi.go.id

*Source: cr18200*

### Section 1

### INDONESIA — REPORT ON RESIDENTIAL PROPERTY PRICE STATISTICS CAPACITY DEVELOPMENT MISSION (FEBRUARY 7–9, 2018) — Section 1

### SUMMARY OF MISSION OUTCOMES AND PRIORITY RECOMMENDATIONS
- Mission: Technical Assistance (TA) mission conducted February 7–9, 2018 to assist Bank Indonesia (BI) with development of the residential property price index (RPPI); advice also provided on the commercial property price index (CPPI).
- Current BI practice:
  - BI compiles and disseminates an RPPI based on quarterly surveys of major developers covering the primary (new) market for single-unit dwellings in 16 cities (with an additional two cities being added).
  - BI currently collects secondary market series based on appraised values from sellers and real estate agents in 10 major cities (series not yet published).
  - BI publishes CPPI statistics based on appraisal data for 7 segments in 10 major cities (11th being added).
- Key findings and guidance:
  - There is scope to redevelop weighting of component indexes to the national total, building on 2016 survey improvements that included more property characteristic detail.
  - Short-term: assess potential for using mortgage data to compile weights, using aggregated value of mortgage drawdowns by region and property type (mortgage data used as weighting observations, not price observations).
  - Longer-term: assess potential for using taxation data to compile weights based on recorded value of residential property transactions by region and property type; develop a strategy for accessing taxation data given Indonesia’s decentralized taxation system.
  - Continue development of an alternative RPPI using Big Data (listings) from real estate web portals to compile robust asking-price indexes for the secondary market using a hedonic methodology.
  - Continue survey-based approach for the primary market; survey approach follows a matched-model methodology so hedonic “in-strata” property mix-adjustment is unnecessary for the primary market.
  - For CPPI, the headline index is an index of activity (incorporates market capitalization rates and rent prices) rather than a pure sales-price index; BI should expand published CPPI statistics to include a national sales price index and a rent price index.
  - Exclude apartments data from future CPPI and include apartments in the RPPI instead.
- Priority recommendations (Table 1):
  - Target Date August 2018 — Develop a strategy for accessing taxation data on property transfers. — Responsible: BI
  - Target Date September 2018 — Assess the potential for using mortgage data to support implementation of improved weighting for property price indexes for the primary market. — Responsible: BI
  - Target Date December 2018 — Compile prototype hedonic RPPIs using listings data for the secondary market in the capital city (Jakarta). — Responsible: BI
  - Target Date May 2019 — Extend the current CPPI publication to include aggregate pure sales price and rent price indexes. — Responsible: BI

### DETAILED TECHNICAL ASSESSMENT AND RECOMMENDATIONS
A. Action Plan — Key milestones and targets
- Outcome: Improved price statistics for Indonesia
- Priority actions and target completion dates:
  - Develop a strategy to secure access to taxation data on property transfers. — Target Completion Date: August 2018
  - Assess the potential for using mortgage data to support the implementation of improved weighting for property price indexes. — Target Completion Date: September 2018
  - Subset residential listings data to include only first instances of each listing. — Target Completion Date: April 2018
  - Develop prototype hedonic RPPI for Jakarta. — Target Completion Date: December 2018
  - Extend prototype to other cities (3 cities, Surabaya, Medan, and Makassar). — Target Completion Date: December 2019
  - Prepare strategy for further development of RPPI. — Target Completion Date: March 2020
  - Extend current CPPI publication to include aggregate pure sales price and rent price indexes. — Target Completion Date: May 2019

B. Introduction — Context
- TA mission follows a diagnostic mission to BI during October 14–16, 2014.
- BI staff from the Real Sector Statistics Division and the Big Data team actively participated.

C. Residential Property Price Index (RPPI) — Technical assessment and recommendations
- Current compilation:
  - RPPI series compiled from quarterly face-to-face surveys of major developers; includes prices, number of units built and sold, expectations, and qualitative data.
  - RPPI covers primary (new) single-unit dwellings in 16 cities (plus two being added).
- Methodology:
  - Survey-based approach implicitly follows a matched-model methodology; no hedonic “in-strata” property mix-adjustment necessary.
  - Current stratification design and index compilation formula assessed as reasonable.
- Data limitations and opportunities:
  - Administrative data (taxation records and mortgage data) currently unsuited as price observations because taxation records lack sufficient property characteristic detail and taxation is decentralized to 34 municipal agencies (non-standardized collection, processing, storage).
  - Short-term use of mortgage data recommended for weighting (aggregated mortgage drawdown values by region and property type).
  - Long-term exploration of taxation data for weighting recommended; strategy should assess feasibility given decentralization and potential need for harmonized administration system. Enhanced taxation records could include limited additional characteristics (property type and vintage: primary, secondary).
- Recommended actions (RPPI):
  - Assess the potential for using mortgage data to compile weights, based on aggregated value of mortgage drawdowns in different regions and for different property types.
  - Develop a strategy for securing access to taxation data on property transfers.

D. The Big Data Project — Use of listings for secondary market RPPI
- Current status:
  - BI receives listings data monthly from three real estate web portals covering 50 cities. Data fields include offer type (rent or sale), asking price, property type, lot size, dwelling size, number of bedrooms, number of bathrooms, address, and free-text characteristics (garage, gated property, swimming pool).
  - Work to date has focused on data preparation, extraction, and prototype indexes using simple mix-adjustment by stratification.
- Recommended focus and methodology:
  - Continue the Big Data project to develop robust asking-price indexes for the secondary market using hedonic methods.
  - Listings data primarily cover the secondary market; continue survey approach for the primary market.
  - Subset listings to include only first instances of each listing at a unique price: include a listing only in the first month it appears, and include updates only in the month of price revision; do not include the same listing price for more than one month.
  - Distinguish rental listings from sale offers; distinguish apartment listings from landed houses.
- Prototype hedonic RPPI for Jakarta — recommended target: by end of 2018
  - Recommended single methodology for initial learning: semi-log rolling-window time-dummy method.
  - Detailed compilation steps:
    - Subset listings to first-instance sales listings for each property at a unique price.
    - Focus on Jakarta and use semi-log rolling window time-dummy method.
    - Initially use core property characteristics only; consider free-text later.
    - Compile and analyze summary statistics for prices and characteristics to inform edit checks and thresholds.
    - Develop appropriate locational stratification balancing granularity with sufficient observations per stratum.
    - Run regressions for 12-month windows and check consistency of diagnostics across windows.
    - Use dummy variables for categorical values; test grouping continuous values as dummies (example: age grouped as new/1-5 years/6-11 years).
    - Apply outlier identification tests (Cook’s distance recommended); remove outliers and rerun regressions.
    - Check plausibility and significance of coefficients and overall model fit/diagnostics.
    - Weight component indexes by value of advertised properties.
- Recommended action (Big Data):
  - Continue the Big Data project, guided by the detailed steps outlined above.

E. The Commercial Property Price Index (CPPI) — Technical assessment and recommendations
- Current compilation:
  - CPPI based on appraisal data from external property market experts; covers 7 segments (offices, retail, apartments, hotels, industrial estates, convention halls, and warehouses) in 10 major cities (11th being added).
  - Data collected via face-to-face interviews with more than 2,700 respondents covering supply, occupancy, sales, and rents.
  - Rent-price and sales-price indexes compiled for segments in each region and weighted to a national index using market capitalization values.
  - Headline CPPI is an index of activity (incorporates market capitalization rates and rent prices), not a pure sales-price index.
- Recommendations:
  - Extend published CPPI statistics to include a national sales price index and a rent price index.
  - Target for publication of expanded statistical release: Quarter 1, 2019 (authorities and mission agreed on this target).
  - Exclude apartments from future CPPI and include apartments in the RPPI.
- Recommended action (CPPI):
  - Extend the current CPPI publication to include a national sale price index and a rent price index.

F. Officials Met During the Mission — Selected contacts
- Ms. Listyowati Puji Lestari — Real Sector Statistics Division, Statistics Department, Bank Indonesia — RPPI — listyowati_p@bi.go.id
- Ms. Herina Prasnawaty D — Real Sector Statistics Division, Statistics Department, Bank Indonesia — RPPI/CPPI — herinapd@bi.go.id
- Ms. Widi Agustin S. (contact details truncated in source)

*Prepared by Niall O’Hanlon; contents based on information available at completion in April 2018.*

### Section 2

### Section 2

### Institutional Unit
- Real Sector Statistics Division
- Statistics Department
- Bank Indonesia
- RPPI/CPPI

### Primary Contacts
- Mr. Irfan Sampe — Big Data Unit, Statistics Department, Bank Indonesia — irfans@bi.go.id
- Mr. Alvin Andhika Zulen — Big Data Unit, Statistics Department, Bank Indonesia — alvin_az@bi.go.id
- Ms. Kumala Kristiawardani — Big Data Unit, Statistics Department, Bank Indonesia — kumala@bi.go.id
- Ms. Hidayah Dhini Ari — Big Data Unit, Statistics Department, Bank Indonesia — dhini_ari@bi.go.id
- Ms. Stephanie Gunawan — Real Sector Statistics Division, Statistics Department, Bank Indonesia — stephanie_g@bi.go.id

### Document Identifiers and Affiliations
- INDONESIA
- INTERNATIONAL MONETARY FUND    11
- RPPI/CPPI
- CPPI
- Contact email: widi_as@bi.go.id

*Source: cr18200 - Section 2*

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_Source: https://www.imf.org/-/media/files/publications/cr/2018/cr18200.pdf_