## 1. Priority Recommendations

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---

### Mission outcomes and high-level findings
- A technical assistance (TA) mission was conducted remotely between May 16 and June 17, 2022, to assist the Bangko Sentral Ng Pilipinas (BSP) with their property price statistics. This was the first mission conducted to the Philippines under the auspices of the Data for Decisions Fund (D4D).
- The mission assisted BSP with final preparations for the introduction of the updated Residential Real Estate Price Index (RREPI) and the new Commercial Property Price Index (CPPI), and provided recommendations for medium and longer-term development of property price statistics.
- Reliable property price indexes are critical inputs for macroprudential policy design and for informing monetary policy and inflation targeting.
- The new methodology for the RREPI and the new CPPI (land only) was evaluated against international compilation standards and best practices; some methodological improvements were recommended and the BSP decided to delay the launch to implement and fully test these changes.
- In the medium term, BSP should introduce hedonic regression for quality adjustment within strata; the information to begin developing hedonic models is available from existing commercial bank data.
- BSP should increase team capacity (training and/or further TA) and transfer processing and aggregation from MS Excel to a statistical software package (e.g., R code, Stata).
- BSP intends to broaden coverage to include cash purchases and other non-bank lending; current quarterly reports from commercial banks cover only part of the market.

### Priority recommendations (action plan and targets)
- August 2022: Implement the recommendations to improve the methodology for the updated RREPI and new CPPI (land only), ensuring that the changes are fully tested. — Responsible: BSP
- August 2022: Disseminate the new indexes with accompanying technical notes and metadata (highlighting the positive improvements in methods and explaining the reasons and extent of the revisions to the RREPI). — Responsible: BSP
- Q4 2022: Assign a dedicated resource to the medium and longer-term research and development work. — Responsible: BSP
- Q2 2023: Increase the capacity of the team with hedonic regression methods through attendance at a training course and/or planning further technical assistance.
- Q4 2023: Transfer the processing and aggregation of the data to a statistical software package e.g., R code, Stata.
- 2024: Use the Inter-Agency Working Group on Property Price Statistics (IAWGPPS) to develop goals, build relationships and negotiate data access.
- 2024: Initiate a pilot project to test if it is possible to link the LRA data with data provided by a single local government unit.

### H. Outcome: Improved property price statistics for Philippines — Priority actions and milestones (selected)
- August 2022: Fully exploit the weights information available by applying them at the level of the strata.
- August 2022: Imputation for a stratum where no transactions are in a quarter.
- August 2022: Maintain homogeneity to the greatest extent possible when stratifying the data for commercial property.
- August 2022: Continue to monitor the number of missing values for key variables such as acquisition cost and appraised value.
- August 2022: Regularly assess outliers to ensure the method is not introducing a bias into the index.
- August 2022: Ensure that the changes to the methods are fully tested before moving forward with dissemination.
- August 2022: Disseminate the new indexes with accompanying technical notes and metadata.
- Q4 2022: Assign a dedicated resource to the medium and longer-term research and development work.
- Q2 2023: Increase team capacity on hedonic regression methods (training/TA).
- Q4 2023: Transfer processing and aggregation to statistical software (R code, Stata).
- 2024: Use IAWGPPS to develop goals and negotiate data access.
- 2024: Initiate an LRA–LGU linkage pilot project.

### A. Data sources — findings and recommended actions
- Current data source: mortgage information collected by BSP from Universal / Commercial Banks (U/KBs) and Thrift Banks (TBs); submitted quarterly within 20 days of the end of the reference quarter.
- BSP internal estimates of transaction shares:
  - commercial banking sector lending accounts for approximately 48 percent of residential property transactions;
  - cash purchases or direct lending by property developers account for about 23 percent of transactions;
  - government backed lending for social housing purposes (mainly by the Home Development Mutual Fund) account for the remaining 29 percent.
- Typical transaction timeline and administrative payments:
  - Seller has 30 days from the date of notarization of the deed of sale to settle capital gains tax and documentary stamp tax with the Bureau of Internal Revenue (BIR).
  - Capital gains tax is calculated at six percent of the total selling price.
  - Documentary stamp tax is calculated at 1.5 percent.
  - Transfer of ownership registration with the Land Registry Authority (LRA) requires a payment of one percent of the declared price.
  - Local transfer charge to the Treasurer’s Office of the Local Government Unit (LGU) is at a rate of between 0.5 percent and 0.75 percent of the declared price.
  - A wealth tax on property (real property tax) is paid to the appropriate local government unit based on property valuations.
- LRA initiatives:
  - LRA is digitalizing title information to create a database of property transactions; LRA captures declared price and property location (e.g., XY coordinates) but lacks detailed property characteristics (e.g., floor area, number of bedrooms).
- LGU data:
  - Treasurer’s Offices of LGUs hold property characteristic information and the title number, which can link to the LRA; however, LGU data is disaggregated across LGUs and not centralized, with potential structural and quality differences across LGUs.
- Recommended actions:
  - Use IAWGPPS to develop shared goals, build relationships and negotiate access to data.
  - Initiate a pilot project to test linkage of LRA data with data from a single LGU to assess data quality, declared price accuracy, linkage feasibility via title number, potential coverage improvements, and timeliness impacts.
  - Consider collecting residential property advertisement data from real estate websites (examples noted in source) as an interim additional data input.

### B. Data cleaning and processing — findings and recommended actions
- Banks submit separate MS Excel electronic reports for residential and commercial property; automatic validation checks (missing values, duplicates, extreme values, consistency) provide respondent feedback and improve data quality.
- BSP should continue to monitor missing values for key variables (e.g., acquisition cost, appraised value) and other variables that may become important for hedonic methods (e.g., number of bedrooms, age of structure, lot area).
- Outlier detection:
  - A generalized box plot method (accounting for skewness and tail heaviness) was chosen for outlier detection to reduce volatility in the RREPI.
  - For residential property, the method excluded 4.8 percent of observations in total over the period 2016 to 2021, split evenly between the upper and lower ends of the distribution.
  - For commercial property, the method removed 5.4 percent of total observations, with 3.0 percent at the lower end and 2.4 percent at the higher end.
  - The number of outliers identified varied by strata and period.
- Recommended actions:
  - Monitor number of missing values for key variables like acquisition cost and appraised value.
  - Regularly assess outliers to ensure the method does not introduce bias into the index.

### C. Stratification, weighting, and aggregation — findings and recommended actions
- Residential price basis:
  - Use of acquisition costs (land and structure) for residential property more accurately represents transaction prices; the updated RREPI will use full acquisition costs. The existing RREPI uses appraised values (buildings only).
- Commercial price basis:
  - Appraised values (land only) will be used to compile indexes for commercial property because only land appraised value is collected; naming conventions considered include Commercial Land Price Index (CLPI) and Commercial Property Land Price Index (CPLPI).
- Stratification:
  - More detailed stratification will be used for commercial and residential property to achieve more homogenous groups.
  - Regional stratification changes: from two strata (NCR and AONCR) to island groups (NCR, Balance Luzon, Visayas, Mindanao), and districts (in NCR) or by cities/municipalities (in AONCR).
  - Resulting number of strata: 30 strata for residential property and 40 strata for commercial property.
  - Maintain homogeneity when stratifying commercial property (four property types: Office, Retail, Mixed-Use and Vacant Lot); e.g., exclude gas stations from Retail stratum.
- Weights and aggregation:
  - Use annually updated fixed weights (three year rolling average) based on share of total sales, aligning with international standards (replacing varying quarter-to-quarter weights based on share of total floor area).
  - Fully exploit weights information by applying them at the level of the strata:
    - Proposed aggregation previously used an unweighted arithmetic mean within strata and an unweighted geometric mean to calculate prices for each combination of island group and type of property; instead BSP should drop the unweighted geometric mean step and directly calculate elementary aggregate indexes at the level of the strata, then use weights to aggregate to higher-level indexes.
    - Benefits: improved accuracy by maximizing use of available weights; enables dissemination of more detailed indexes (e.g., districts within NCR).
- Imputation:
  - Apply imputation for a stratum with no transactions in a quarter:
    - For strata in NCR: use weighted average price change across other districts for imputation.
    - For strata in AONCR: use price change in cities to impute for municipalities and vice versa.
    - If no transactions within the entire island group and property type combination, use price change from the next closest stratum based on compiler judgement.

### Implementation decision
- The authorities decided to delay the launch of the updated RREPI and new CPPI to allow time to implement some of the mission recommendations.
- The BSP will implement the recommendation to fully exploit the available information for the weights.
- "The changes should be fully tested before moving forward with dissemination."

### Methodological improvements and medium-term actions
- Current aggregation approach: stratification with arithmetic averages to compute the price indexes; controls for quality-mix adjustment between strata, but not within strata.
- Recommended international approach: use hedonic regression methods for quality adjustment within strata.
- BSP already collects information required for regression models, e.g., number of floors, number of bedrooms, age of the property and lot size.
- Capacity building recommended: increase team capacity through attendance at a training course and/or planning further technical assistance specifically on hedonic methods.
- These skills are important whether the data source remains the same or changes to a new administrative data source.
- Recommended Actions (methodology):
  - Consider alternative names for the CPPI to clarify that it includes only land values.
  - Maintain homogeneity to the greatest extent possible when stratifying the data for commercial property.
  - Fully exploit the weights information available by applying them at the level of the strata.
  - Apply imputation for a stratum where there are no transactions in a particular quarter.
  - Ensure the changes to the methods are fully tested before moving on with dissemination.
  - In the medium term, consider using hedonic regression methods for quality adjustment within strata.
  - Increase the capacity of the team with hedonic regression methods through attendance at a training course and/or planning further technical assistance.
  - Transfer processing and aggregation of data to statistical software package (R code, Stata).

### Processing and software
- Current compilation completed in MS Excel.
- Recommendation: transfer processing and aggregation of the data to a statistical software package e.g., R code, Stata.
- Rationale: facilitates further methodological improvements (such as hedonic methods), increases processing stability, and reduces the risk of processing errors.

### Dissemination: advance notice, documentation, and user communication
- Mission discussed advance notice provided to users, layout of statistical commentary, the technical (metadata) document, and the press release.
- Authorities should highlight positive improvements in compilation methods for RREPI:
  - use of acquisition costs instead of appraisal values,
  - introduction of a robust method for outlier detection,
  - application of more detailed stratification,
  - use of annually updated fixed weights.
- These combined represent a significant improvement in the quality of the RREPI.
- Revisions and user transition:
  - Proposal: revise the full historical series for the RREPI, beginning in Q1 2016.
  - Users will need to transition from the existing series and understand reasons for significant differences between the existing series and the updated RREPI.
  - BSP should publish graphs with both the existing and updated indexes, highlighting (to the extent possible) why the updated RREPI deviates from the existing index.
  - Graphical analysis could be included in the technical (metadata) document or published as a separate document.
  - Archived publications should be updated to alert users that the RREPI has been revised.
- Technical (metadata) document enhancements:
  - Include the date that the RREPI was revised and the date the CPPI was launched for the first time.
  - Provide additional detail on data cleaning, missing values, outlier detection to assure users about data quality.
  - Include information on the calculation of key dissemination indicators such as percent price change and the quarterly and annual contributions.
- Annual contributions formula:
  - The formula for the calculation of the annual contributions needs to be modified due to the move to annual updating of weights.
  - When expenditure weights are annually updated, price indexes spanning more than a year are chain-linked and the formula to compute annual contributions to inflation should be modified.
  - The mission presented the new formula and suggested allowing sufficient time to update the calculations.
- Recommended Actions (dissemination):
  - The authorities should highlight the improvements in the compilation methods for RREPI.
  - Clear communication is required for users in relation to the revisions to the RREPI.
  - Include additional sections in the technical (metadata) document e.g., methods for ensuring high quality data, details on the calculation of key dissemination indicators.
  - The formula for the calculation of the annual contributions should be modified due to the move to annual updating of weights.

### Resources
- The BPS should assign a dedicated resource to the medium and longer-term research and development work.
- Rationale: planned work includes hedonic regression methods for quality adjustment, transition to a statistical software package, building relationships with external organizations, and development of new data sources.
- A dedicated resource should be separate from resources assigned to the quarterly production of the RREPI and CPPI.
- Recommended Action (resources):
  - Assign a dedicated resource to the medium and long-term research and development work.

### Officials met during the mission
- Ms. Haydee R. Paulino — BSP
- Ms. Willa Boots J. Tolo — BSP
- Mr. Jeremy L. De Jesus — BSP
- Mr. Gian Paolo T. De La Torre — BSP
- Mr. Richer King R. Supnet — BSP
- Ms. Gloria A. Cubinar — BSP

### Annex I — Timeline of a Typical Property Transaction in the Philippines (selected items)
- 1 Property is placed in the market.
  - Price: Asking price
  - Data sources: Real estate brokers associations (e.g., Real Estate Brokers Association of the Philippines, Inc. (REBAP), Philippine Association of Real Estate Boards (PAREB)); Websites (e.g., Lamudi, Zipmatch, Dot Property, Craigslist, Carousell); Newspaper classified ads; Word-of-mouth.
  - Timeline: 4 weeks to 2 years.
- 2 Price is agreed between buyer and seller.
  - Price: Transaction price
  - Data source: Contract to sell (public and notarized) – notary public.
  - Timeline: 1 week (conditional approval).
- 3a Mortgage is approved.
  - Price: Valuation price
  - Data sources: Banks; Non-banks (Home Development Mutual Fund (HDMF), National Housing Authority (NHA), National Home Mortgage Finance Corporation (NHMFC), Social Housing Finance Corporation (SHFC)).
  - Timeline: 5 days-3 weeks after document requirements submission.
- 3a Letter of guarantee issued.
  - Price: Valuation price
  - Timeline: Within days.
- 3b In-house financing is approved.
  - Price: Transaction price
  - Data source: Developers
  - Timeline: 12 weeks.
- 4 Contract is signed (property paid).
  - Price: Declared price/ Transaction price.
  - Data sources: Notarized deed of absolute sale; Unnotarized deed of absolute sale (between buyers and sellers).
- 5 Capital gains tax and documentary stamp tax are paid (6 and 1.5 percent).
  - Price: Declared price/ Fair market value/ Zonal value, which is higher.
  - Data source: Bureau of Internal Revenue (BIR)
  - Timeline: Tax liability must be paid within 30 days of the sale.
- 6 Transfer of ownership registered (Fee: 1 percent of price).
  - Price: Declared price
  - Data source: Land Registration Authority (LRA)
  - Timeline: 4 weeks.
- 7 Mortgage is registered (the property title is annotated with encumbrance).
  - Price: Declared price
  - Data source: Land Registration Authority (LRA)
  - Timeline: 1-4 weeks.
- 8 Local transfer tax is paid (0.5-0.75 percent of price).
  - Price: Declared price
  - Data source: Treasurer’s Office of the Local Government Unit (LGU).
  - Timeline: 1 day to 1 week.
- 9 Tax declaration form (assessment of real property tax) is filed.
  - Price: Zonal price
  - Data source: Assessor’s Office / Treasurer’s Office of the Local Government Unit (LGU).
  - Timeline: 4 weeks.

*International Monetary Fund — PHILIPPINES, "1. Priority Recommendations" (excerpt).*

### 1. Priority Recommendations _____________________________________________________________________ 5

### 1phlea2023001 - 1. Priority Recommendations

### Mission outcomes and high-level findings
- A technical assistance (TA) mission was conducted remotely between May 16 and June 17, 2022, to assist the Bangko Sentral Ng Pilipinas (BSP) with their property price statistics. This was the first mission conducted to the Philippines under the auspices of the Data for Decisions Fund (D4D).
- The mission assisted BSP with final preparations for the introduction of the updated Residential Real Estate Price Index (RREPI) and the new Commercial Property Price Index (CPPI), and provided recommendations for medium and longer-term development of property price statistics.
- Reliable property price indexes are critical inputs for macroprudential policy design and for informing monetary policy and inflation targeting.
- The new methodology for the RREPI and the new CPPI (land only) was evaluated against international compilation standards and best practices; some methodological improvements were recommended and the BSP decided to delay the launch to implement and fully test these changes.
- In the medium term, BSP should introduce hedonic regression for quality adjustment within strata; the information to begin developing hedonic models is available from existing commercial bank data.
- BSP should increase team capacity (training and/or further TA) and transfer processing and aggregation from MS Excel to a statistical software package (e.g., R code, Stata).
- BSP intends to broaden coverage to include cash purchases and other non-bank lending; current quarterly reports from commercial banks cover only part of the market.

### Priority recommendations (action plan and targets)
- August 2022: Implement the recommendations to improve the methodology for the updated RREPI and new CPPI (land only), ensuring that the changes are fully tested. — Responsible: BSP
- August 2022: Disseminate the new indexes with accompanying technical notes and metadata (highlighting the positive improvements in methods and explaining the reasons and extent of the revisions to the RREPI). — Responsible: BSP
- Q4 2022: Assign a dedicated resource to the medium and longer-term research and development work. — Responsible: BSP
- Q2 2023: Increase the capacity of the team with hedonic regression methods through attendance at a training course and/or planning further technical assistance.
- Q4 2023: Transfer the processing and aggregation of the data to a statistical software package e.g., R code, Stata.
- 2024: Use the Inter-Agency Working Group on Property Price Statistics (IAWGPPS) to develop goals, build relationships and negotiate data access.
- 2024: Initiate a pilot project to test if it is possible to link the LRA data with data provided by a single local government unit.

### Detailed technical assessment and recommended actions

H. Outcome: Improved property price statistics for Philippines — Priority actions and milestones (selected)
- August 2022: Fully exploit the weights information available by applying them at the level of the strata.
- August 2022: Imputation for a stratum where no transactions are in a quarter.
- August 2022: Maintain homogeneity to the greatest extent possible when stratifying the data for commercial property.
- August 2022: Continue to monitor the number of missing values for key variables such as acquisition cost and appraised value.
- August 2022: Regularly assess outliers to ensure the method is not introducing a bias into the index.
- August 2022: Ensure that the changes to the methods are fully tested before moving forward with dissemination.
- August 2022: Disseminate the new indexes with accompanying technical notes and metadata.
- Q4 2022: Assign a dedicated resource to the medium and longer-term research and development work.
- Q2 2023: Increase team capacity on hedonic regression methods (training/TA).
- Q4 2023: Transfer processing and aggregation to statistical software (R code, Stata).
- 2024: Use IAWGPPS to develop goals and negotiate data access.
- 2024: Initiate an LRA–LGU linkage pilot project.

A. Data sources — findings and recommended actions
- Current data source: mortgage information collected by BSP from Universal / Commercial Banks (U/KBs) and Thrift Banks (TBs); submitted quarterly within 20 days of the end of the reference quarter.
- BSP internal estimates of transaction shares:
  - commercial banking sector lending accounts for approximately 48 percent of residential property transactions;
  - cash purchases or direct lending by property developers account for about 23 percent of transactions;
  - government backed lending for social housing purposes (mainly by the Home Development Mutual Fund) account for the remaining 29 percent.
- Typical transaction timeline and administrative payments:
  - Seller has 30 days from the date of notarization of the deed of sale to settle capital gains tax and documentary stamp tax with the Bureau of Internal Revenue (BIR).
  - Capital gains tax is calculated at six percent of the total selling price.
  - Documentary stamp tax is calculated at 1.5 percent.
  - Transfer of ownership registration with the Land Registry Authority (LRA) requires a payment of one percent of the declared price.
  - Local transfer charge to the Treasurer’s Office of the Local Government Unit (LGU) is at a rate of between 0.5 percent and 0.75 percent of the declared price.
  - A wealth tax on property (real property tax) is paid to the appropriate local government unit based on property valuations.
- LRA initiatives:
  - LRA is digitalizing title information to create a database of property transactions; LRA captures declared price and property location (e.g., XY coordinates) but lacks detailed property characteristics (e.g., floor area, number of bedrooms).
- LGU data:
  - Treasurer’s Offices of LGUs hold property characteristic information and the title number, which can link to the LRA; however, LGU data is disaggregated across LGUs and not centralized, with potential structural and quality differences across LGUs.
- Recommended actions:
  - Use IAWGPPS to develop shared goals, build relationships and negotiate access to data.
  - Initiate a pilot project to test linkage of LRA data with data from a single LGU to assess data quality, declared price accuracy, linkage feasibility via title number, potential coverage improvements, and timeliness impacts.
  - Consider collecting residential property advertisement data from real estate websites (examples noted in source) as an interim additional data input.

B. Data cleaning and processing — findings and recommended actions
- Banks submit separate MS Excel electronic reports for residential and commercial property; automatic validation checks (missing values, duplicates, extreme values, consistency) provide respondent feedback and improve data quality.
- BSP should continue to monitor missing values for key variables (e.g., acquisition cost, appraised value) and other variables that may become important for hedonic methods (e.g., number of bedrooms, age of structure, lot area).
- Outlier detection:
  - A generalized box plot method (accounting for skewness and tail heaviness) was chosen for outlier detection to reduce volatility in the RREPI.
  - For residential property, the method excluded 4.8 percent of observations in total over the period 2016 to 2021, split evenly between the upper and lower ends of the distribution.
  - For commercial property, the method removed 5.4 percent of total observations, with 3.0 percent at the lower end and 2.4 percent at the higher end.
  - The number of outliers identified varied by strata and period.
- Recommended actions:
  - Monitor number of missing values for key variables like acquisition cost and appraised value.
  - Regularly assess outliers to ensure the method does not introduce bias into the index.

C. Stratification, weighting, and aggregation — findings and recommended actions
- Residential price basis:
  - Use of acquisition costs (land and structure) for residential property more accurately represents transaction prices; the updated RREPI will use full acquisition costs. The existing RREPI uses appraised values (buildings only).
- Commercial price basis:
  - Appraised values (land only) will be used to compile indexes for commercial property because only land appraised value is collected; naming conventions considered include Commercial Land Price Index (CLPI) and Commercial Property Land Price Index (CPLPI).
- Stratification:
  - More detailed stratification will be used for commercial and residential property to achieve more homogenous groups.
  - Regional stratification changes: from two strata (NCR and AONCR) to island groups (NCR, Balance Luzon, Visayas, Mindanao), and districts (in NCR) or by cities/municipalities (in AONCR).
  - Resulting number of strata: 30 strata for residential property and 40 strata for commercial property.
  - Maintain homogeneity when stratifying commercial property (four property types: Office, Retail, Mixed-Use and Vacant Lot); e.g., exclude gas stations from Retail stratum.
- Weights and aggregation:
  - Use annually updated fixed weights (three year rolling average) based on share of total sales, aligning with international standards (replacing varying quarter-to-quarter weights based on share of total floor area).
  - Fully exploit weights information by applying them at the level of the strata:
    - Proposed aggregation previously used an unweighted arithmetic mean within strata and an unweighted geometric mean to calculate prices for each combination of island group and type of property; instead BSP should drop the unweighted geometric mean step and directly calculate elementary aggregate indexes at the level of the strata, then use weights to aggregate to higher-level indexes.
    - Benefits: improved accuracy by maximizing use of available weights; enables dissemination of more detailed indexes (e.g., districts within NCR).
- Imputation:
  - Apply imputation for a stratum with no transactions in a quarter:
    - For strata in NCR: use weighted average price change across other districts for imputation.
    - For strata in AONCR: use price change in cities to impute for municipalities and vice versa.
    - If no transactions within the entire island group and property type combination, use price change from the next closest stratum based on compiler judgement.

*International Monetary Fund — PHILIPPINES, "1. Priority Recommendations" (excerpt).*

### 25.      The authorities decided to delay the launch of the updated RREPI and new CPPI to

### 1phlea2023001 - 25.      The authorities decided to delay the launch of the updated RREPI and new CPPI to

### Implementation decision
- The authorities decided to delay the launch of the updated RREPI and new CPPI to allow time to implement some of the mission recommendations.
- The BSP will implement the recommendation to fully exploit the available information for the weights.
- "The changes should be fully tested before moving forward with dissemination."

### Methodological improvements and medium-term actions
- Current aggregation approach: stratification with arithmetic averages to compute the price indexes; controls for quality-mix adjustment between strata, but not within strata.
- Recommended international approach: use hedonic regression methods for quality adjustment within strata.
- BSP already collects information required for regression models, e.g., number of floors, number of bedrooms, age of the property and lot size.
- Capacity building recommended: increase team capacity through attendance at a training course and/or planning further technical assistance specifically on hedonic methods.
- These skills are important whether the data source remains the same or changes to a new administrative data source.

Recommended Actions (methodology)
- Consider alternative names for the CPPI to clarify that it includes only land values.
- Maintain homogeneity to the greatest extent possible when stratifying the data for commercial property.
- Fully exploit the weights information available by applying them at the level of the strata.
- Apply imputation for a stratum where there are no transactions in a particular quarter.
- Ensure the changes to the methods are fully tested before moving on with dissemination.
- In the medium term, consider using hedonic regression methods for quality adjustment within strata.
- Increase the capacity of the team with hedonic regression methods through attendance at a training course and/or planning further technical assistance.
- Transfer processing and aggregation of data to statistical software package (R code, Stata).

### Processing and software
- Current compilation completed in MS Excel.
- Recommendation: transfer processing and aggregation of the data to a statistical software package e.g., R code, Stata.
- Rationale: facilitates further methodological improvements (such as hedonic methods), increases processing stability, and reduces the risk of processing errors.

### Dissemination: advance notice, documentation, and user communication
- Mission discussed advance notice provided to users, layout of statistical commentary, the technical (metadata) document, and the press release.
- Authorities should highlight positive improvements in compilation methods for RREPI:
  - use of acquisition costs instead of appraisal values,
  - introduction of a robust method for outlier detection,
  - application of more detailed stratification,
  - use of annually updated fixed weights.
- These combined represent a significant improvement in the quality of the RREPI.

Revisions and user transition
- Proposal: revise the full historical series for the RREPI, beginning in Q1 2016.
- Users will need to transition from the existing series and understand reasons for significant differences between the existing series and the updated RREPI.
- BSP should publish graphs with both the existing and updated indexes, highlighting (to the extent possible) why the updated RREPI deviates from the existing index.
- Graphical analysis could be included in the technical (metadata) document or published as a separate document.
- Archived publications should be updated to alert users that the RREPI has been revised.

Technical (metadata) document enhancements
- Include the date that the RREPI was revised and the date the CPPI was launched for the first time.
- Provide additional detail on data cleaning, missing values, outlier detection to assure users about data quality.
- Include information on the calculation of key dissemination indicators such as percent price change and the quarterly and annual contributions.

Annual contributions formula
- The formula for the calculation of the annual contributions needs to be modified due to the move to annual updating of weights.
- When expenditure weights are annually updated, price indexes spanning more than a year are chain-linked and the formula to compute annual contributions to inflation should be modified.
- The mission presented the new formula and suggested allowing sufficient time to update the calculations.

Recommended Actions (dissemination)
- The authorities should highlight the improvements in the compilation methods for RREPI.
- Clear communication is required for users in relation to the revisions to the RREPI.
- Include additional sections in the technical (metadata) document e.g., methods for ensuring high quality data, details on the calculation of key dissemination indicators.
- The formula for the calculation of the annual contributions should be modified due to the move to annual updating of weights.

### Resources
- The BPS should assign a dedicated resource to the medium and longer-term research and development work.
- Rationale: planned work includes hedonic regression methods for quality adjustment, transition to a statistical software package, building relationships with external organizations, and development of new data sources.
- A dedicated resource should be separate from resources assigned to the quarterly production of the RREPI and CPPI.

Recommended Action (resources)
- Assign a dedicated resource to the medium and long-term research and development work.

### Officials met during the mission
- Ms. Haydee R. Paulino — BSP
- Ms. Willa Boots J. Tolo — BSP
- Mr. Jeremy L. De Jesus — BSP
- Mr. Gian Paolo T. De La Torre — BSP
- Mr. Richer King R. Supnet — BSP
- Ms. Gloria A. Cubinar — BSP

### Annex I — Timeline of a Typical Property Transaction in the Philippines (selected items)
- 1 Property is placed in the market.
  - Price: Asking price
  - Data sources: Real estate brokers associations (e.g., Real Estate Brokers Association of the Philippines, Inc. (REBAP), Philippine Association of Real Estate Boards (PAREB)); Websites (e.g., Lamudi, Zipmatch, Dot Property, Craigslist, Carousell); Newspaper classified ads; Word-of-mouth.
  - Timeline: 4 weeks to 2 years.
- 2 Price is agreed between buyer and seller.
  - Price: Transaction price
  - Data source: Contract to sell (public and notarized) – notary public.
  - Timeline: 1 week (conditional approval).
- 3a Mortgage is approved.
  - Price: Valuation price
  - Data sources: Banks; Non-banks (Home Development Mutual Fund (HDMF), National Housing Authority (NHA), National Home Mortgage Finance Corporation (NHMFC), Social Housing Finance Corporation (SHFC)).
  - Timeline: 5 days-3 weeks after document requirements submission.
- 3a Letter of guarantee issued.
  - Price: Valuation price
  - Timeline: Within days.
- 3b In-house financing is approved.
  - Price: Transaction price
  - Data source: Developers
  - Timeline: 12 weeks.
- 4 Contract is signed (property paid).
  - Price: Declared price/ Transaction price.
  - Data sources: Notarized deed of absolute sale; Unnotarized deed of absolute sale (between buyers and sellers).
- 5 Capital gains tax and documentary stamp tax are paid (6 and 1.5 percent).
  - Price: Declared price/ Fair market value/ Zonal value, which is higher.
  - Data source: Bureau of Internal Revenue (BIR)
  - Timeline: Tax liability must be paid within 30 days of the sale.
- 6 Transfer of ownership registered (Fee: 1 percent of price).
  - Price: Declared price
  - Data source: Land Registration Authority (LRA)
  - Timeline: 4 weeks.
- 7 Mortgage is registered (the property title is annotated with encumbrance).
  - Price: Declared price
  - Data source: Land Registration Authority (LRA)
  - Timeline: 1-4 weeks.
- 8 Local transfer tax is paid (0.5-0.75 percent of price).
  - Price: Declared price
  - Data source: Treasurer’s Office of the Local Government Unit (LGU).
  - Timeline: 1 day to 1 week.
- 9 Tax declaration form (assessment of real property tax) is filed.
  - Price: Zonal price
  - Data source: Assessor’s Office / Treasurer’s Office of the Local Government Unit (LGU).
  - Timeline: 4 weeks.

*Source: PHILIPPINES — INTERNATIONAL MONETARY FUND (excerpt).*

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_Source: https://www.imf.org/-/media/files/publications/cr/2023/english/1phlea2023001.pdf_