2018年-IMF国际货币组织全球_Indonesia_Technical_Assistance_Report_–_Residential_Property_Price_Statistics_Capacity_Development_Mission_12页_628kb

Indonesia: Residential Property Price Statistics Capacity Development Mission Summary

Core Content Overview

This Technical Assistance (TA) report outlines the findings and recommendations of a mission conducted in February 2018 to support Bank Indonesia (BI) in improving its residential and commercial property price statistics. The mission aimed to enhance the accuracy, comprehensiveness, and reliability of property price indexes, which are essential for assessing market developments, risks, and linkages with financial soundness, as well as for IMF surveillance.

Main Recommendations and Actions

1. Residential Property Price Index (RPPI)

• Current Methodology: BI compiles the RPPI using quarterly surveys of major developers, focusing on the primary (new) market for single-unit dwellings in 16 cities (with two more being added).
• Survey-Based Approach: The RPPI implicitly follows a matched-model methodology, using a fixed panel of properties, which avoids the need for hedonic "in-strata" adjustments.
• Secondary Market Data: BI has collected data for the secondary market (existing properties) from surveys of sellers and agents in 10 major cities, but these have not yet been published.
• Weighting Improvements:
  • In the short-term, BI should assess the potential of using mortgage data to derive weights based on the aggregated value of mortgage drawdowns across regions and property types.
  • In the long-term, BI could consider taxation data to better represent the national market structure, though this would require a harmonized administration system.
• Big Data Project:
  • BI is developing an alternative RPPI using Big Data from real estate web portals, which provide detailed listings including price, property type, and other characteristics.
  • The mission recommended that the project focus on the secondary market, especially in Jakarta, and use a semi-log rolling-window time-dummy method for hedonic index compilation.
  • BI should continue the survey-based approach for the primary market due to the lack of suitable alternative data.

2. Commercial Property Price Index (CPPI)

• Current Approach: BI compiles the CPPI using appraisal data from external property market experts, covering 7 segments in 10 major cities.
• Headline Index: The CPPI is an activity index that incorporates market capitalization rates and rent prices, rather than a pure sales price index.
• Recommended Expansion:
  • BI should extend the CPPI publication to include a national sale price index and a rent price index.
  • This expansion aligns with the data needs for macro-prudential policy formulation.
• Target Publication Date: The expanded statistical release is targeted for Q1 2019.

Priority Recommendations (Table)

Key Considerations

• Data Sources:
  • The current RPPI relies on developer surveys, while the CPPI uses expert appraisals.
  • Administrative data (e.g., taxation and mortgage records) are not yet suitable for price observations due to lack of granularity.
• Big Data Potential:
  • Offers a promising avenue for compiling robust asking price indexes in the secondary market.
  • Requires careful data processing and stratification to balance granularity and data sufficiency.
• Challenges:
  • Decentralized taxation administration in Indonesia complicates data collection.
  • The use of taxation data for weighting may require a harmonized system and coordination across municipal agencies.
• Implementation Status:
  • The mission noted that the Big Data project has made significant progress in data preparation and extraction.
  • BI is actively working on prototype indexes and regression models for Jakarta.

Officials Met During the Mission

• Ms. Listyowati Puji Lestari – Real Sector Statistics Division, BI
• Ms. Herina Prasnawaty D – Real Sector Statistics Division, BI
• Ms. Widi Agustin S. – Real Sector Statistics Division, BI
• Mr. Irfan Sampe – Big Data Unit, BI
• Mr. Alvin Andhika Zulen – Big Data Unit, BI
• Ms. Kumala Kristiawardani – Big Data Unit, BI
• Ms. Hidayah Dhini Ari – Big Data Unit, BI
• Ms. Stephanie Gunawan – Real Sector Statistics Division, BI

Conclusion

The mission emphasized the importance of improving the accuracy and coverage of property price statistics for both residential and commercial sectors in Indonesia. BI is encouraged to continue its work on the RPPI and CPPI, leveraging Big Data and exploring taxation data for long-term improvements. The recommendations aim to enhance the reliability and comprehensiveness of these indexes, supporting better monetary policy and financial stability assessments.