## INTRODUCTION

## Source details

**Canonical URL:** [INTRODUCTION](https://www.imf.org/-/media/files/publications/gfs-notes/2022/english/gfsnea2022002.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/gfs-notes/2022/english/gfsnea2022002.pdf.md)
- [Structured JSON version](/-/media/files/publications/gfs-notes/2022/english/gfsnea2022002.pdf.json)

---

### Pre‑pandemic trends and COVID‑19 reversal
- CRE prices rose annually at about 6 percent on average during 2009–19, with North America experiencing the steepest increase.
- At the onset of the pandemic CRE prices plummeted, particularly in Europe and North America, and in the retail segment.
- Aggregate CRE prices started to rebound in late 2020 and early 2021, but recovery across regions and segments was uneven:
  - North America: aggregate CRE prices increased noticeably driven by a surge in residential and industrial property prices; retail and office recovery lagged.
  - Asia and Europe: average increases smaller than North America; office and retail performed relatively better in 2021.
- CRE transaction volumes fell by 20 percent to 70 percent across regions during the decline and may have limited further price declines.
- Sample: quarterly CRE price data for 96 metropolitan cities from 31 major advanced and emerging market economies, 2016 to 2021.

### Dispersion in CRE prices: stylized facts and cross‑sectional patterns
- Pandemic vs pre‑pandemic dispersion:
  - The standard deviation of changes in aggregate CRE prices across cities was almost twice as large in the pandemic (2020:Q1–2021:Q4) than in the pre‑pandemic period (2016:Q1–2019:Q4).
  - Dispersion across CRE segments within cities was also much larger during the pandemic than before.
- City and regional examples:
  - Canada: aggregate CRE prices fell, on average, by 2 percent as the pandemic evolved in the first three quarters of 2020; within Canada, Regina recorded about a 4 percent decline (quarter‑over‑quarter average) while Toronto remained stable.
  - Global cities: Johannesburg recorded the largest drop (3 percent) during the first three quarters of 2020; Berlin and Oslo experienced the largest increases (of 3–5 percent, respectively). On average, CRE prices rose slightly (0.3 percent) in global cities during the early phase of the pandemic but declined by 0.7 percent elsewhere.
  - Retail: prices fell by about 7 percent in some cities (e.g., Baltimore, Glasgow, Minneapolis) in 2020:Q1–Q3 but increased in others (e.g., Austin, Kuala Lumpur, Seoul).
- Segment performance (2020:Q1–Q3):
  - Retail: average quarterly decline about 2.5 percent.
  - Office: prices remained stable on average, though some cities saw declines between 2 percent to 6 percent.
  - Residential: prices increased by about 1 percent.
  - Industrial: prices increased by about 1.3 percent.
- Performance since end‑2020 (2020:Q4–2021:Q4):
  - Cities in Australia, New Zealand, and the United States showed a rebound in CRE prices in 2021, while prices continued to fall in several other cities (e.g., Canada, Ireland, South Africa).
  - Retail segment: average quarterly decline in 2020:Q4–2021:Q4 was 1.2 percent (smaller than the 2.5 percent decline in 2020:Q1–Q3).
  - Office sector: prices fell by 0.2 percent on average in 2020:Q4–2021:Q4, compared to a small increase of 0.1 percent during 2020:Q1–Q3.
  - Overall, about 77 percent of the cities in the sample experienced a decline in retail prices in 2020:Q4–2021:Q4 (as compared to 84 percent in 2020:Q1–Q3).
- Notable micro patterns:
  - Residential resilience in many areas contrasted with declines in densely populated central locations, consistent with household migration to less crowded suburban areas.

### Drivers and conceptual framework
- Commercial property prices depend on current and expected net operating income growth and returns; vacancy rates, economic activity, and financial market conditions are key.
- Pandemic‑specific drivers included containment measures, pandemic spread, policy support, and behavioral shifts (teleworking, e‑commerce).
- Vacancy rates generally rose during the pandemic, especially where containment measures were more stringent.

### 1. Overall Fiscal Stimulus

### Effects and magnitudes (panels summarized)
- Panel definitions used in analysis:
  - Panel 1: effect of one standard deviation increase in fiscal spending to GDP on CRE price changes. Standard error bars indicate 90 percent confidence interval.
  - Panel 2: effect of a one percent of GDP increase in discretionary spending (percentage points).
  - Panel 3: effect of a one standard deviation increase in total government equity injections, loans, and guarantees during the pandemic (about 6 percent of GDP) on the change in CRE prices.
  - Panel 4: effect of one standard deviation increase in the debt and contract relief index on the change in CRE prices.
- Data sources: IMF Fiscal Monitor database; MSCI Real Estate; Oxford COVID-19 Government Response Tracker; and authors’ calculations.
- Statistical reporting: all panels report standard error bars reflecting a 90 percent confidence interval.

### Key empirical finding
- Larger fiscal policy support and easier financial conditions helped to cushion CRE price declines during the pandemic.
  - A one standard deviation increase in stimulus spending (equivalent to 9 percent of GDP) is associated with a 0.1 percentage point smaller quarterly decline in overall CRE prices.
  - Public liquidity support (equity injections, loans, guarantees) mostly benefitted retail and office segments.
  - Discretionary spending particularly helped retail, residential, and industrial segments.
  - Debt and contract relief to households is associated with significantly smaller price declines across CRE segments.

### Policy implications (fiscal / liquidity perspective)
- Targeted fiscal responses and public liquidity support play a cushioning role during severe real estate shocks and are important for policy design.

### CRE market sentiment and financial conditions
- Investor and occupier sentiment indicators measure proportion reporting a rise minus those reporting a fall.
- Financial Conditions Index: total effect of a one standard deviation increase during the pandemic reported as sum of pre‑pandemic marginal impact and additional post‑pandemic impact; standard errors at 90 percent confidence interval.
- Empirical note: absence of wide‑scale deleveraging and lower distressed deals compared with earlier downturns (for example, the global financial crisis and the early 1990s recession) is consistent with policy support and looser financial conditions.
- Monetary easing by major central banks lifted CRE prices; potential tightening during normalization could adversely affect CRE via higher financing costs and slower economic activity.

### Behavioral Shifts

### Teleworking
- United States survey magnitudes:
  - "42 percent of the labor force in the United States—accounting for two-thirds of economic activity—was working from home full time in the second quarter of 2020."
  - "About 26 percent was working onsite at the business premises" in the same period.
  - "Only 6 percent of those employed in the United Stated in 2019 worked primarily from home and about three-quarters had never worked from home (Coate 2021)."
- Empirical effects:
  - Cities with a higher share of teleworkable jobs (Dingel and Neiman’s (2020) teleworkability index) experienced larger declines in office property prices, particularly in the initial phase of the pandemic.
  - Residential property prices were also lower during the pandemic in regions with higher teleworkability, consistent with relocation out of urban areas.
- Estimation note: panels report additional effect of a one standard deviation increase in the teleworkability index on quarterly office property price growth during 2020:Q1−2021:Q4 and in the initial phase (first three quarters of 2020); standard error bars reflect a 90 percent confidence interval.

### E‑commerce
- Observed shifts:
  - "Sales in the United States jumped by 43 percent in 2020 relative to the year before to reach almost 15 percent of total retail trade (Brewster 2022)."
- Empirical effects:
  - A one standard deviation increase in the UNCTAD e‑commerce index is associated with:
    - "a 0.5 percentage point reduction in prices in the retail segment"
    - "a 0.9 percentage point increase in the industrial segment."
  - Panel results compare effects before the pandemic (2016:Q1−2019:Q4) and additional effect after the pandemic (2020:Q1−2021:Q4); standard error bars reflect a 90 percent confidence interval.
- Measurement caveat: e‑commerce index is available only at country level and annually and does not capture within‑country variation.

### Implications of behavioral shifts
- Office and retail segments may remain under pressure in the near to medium term if teleworking and e‑commerce trends persist.
- Industrial segment benefitted from e‑commerce but may see more moderate gains as vaccination encourages some return to in‑store retail.
- Potential offsetting forces include demand for alternative uses (office‑to‑residential conversions; retail‑to‑warehouse/fulfillment‑center conversions).

### Other Factors Affecting CRE Prices

### House prices and momentum
- Commercial property prices are generally positively associated with house prices.
- Variation in house prices increased across cities during the pandemic—particularly in Asia and Europe—contributing to increased variation in CRE prices.
- Price momentum (lagged CRE price growth) is important:
  - Cities with higher pre‑pandemic price growth were less affected initially and subsequently experienced faster price growth, controlling for other factors.

### Cross‑sectional and country heterogeneity
- Variation in containment measures, pandemic spread, fiscal support, teleworkability, and e‑commerce potential explain cross‑sectional differences in CRE price outcomes.
- Increased heterogeneity in the pace of economic recovery contributed to large variation observed in retail prices.
- Empirical dispersion: the standard deviation of quarterly growth in real house prices in the sample increased by a factor of approximately 1.5 between the pre‑pandemic and pandemic periods.

### 1. Global Bust and Recovery

### Empirical framework (baseline and extensions)
- Baseline regression (equation [1]) — dependent variable: quarterly real commercial property price growth (in percent); index notation: i (city), c (country), t (quarter).
- Baseline regressors include:
  - Lagged CRE price growth: CRE price growth_i,c,t−1
  - Financial Condition Index_c,t
  - Stringency Index_i,c,t
  - Change in COVID cases_c,t (quarterly percentage change in confirmed COVID-19 cases)
  - Fiscal Measures_c,t (additional economic stimulus spending as percent of GDP reported by the government in each quarter during the pandemic)
  - Real GDP growth_c,t+4 (projected one-year ahead real GDP growth from IMF WEO)
  - Change in House Prices_c,t (quarterly growth in house prices)
  - COVID_t (indicator = 1 in 2020:Q1−2021:Q4, and zero otherwise)
  - Interactions: X_i,c,t × COVID_t for non‑pandemic‑specific variables
  - Quarter fixed effects D_q,t and city fixed effects α_i
  - Error term ε_i,c,t
- Estimation notes:
  - Lagged dependent variable captures momentum; Nickell bias (approx equal to 1/T) acknowledged.
  - Robustness: country‑level fixed effects and specifications without lagged dependent variable yield robust results.
  - Values above the 99th percentile excluded for certain variables to avoid outlier bias.
  - Projected future real GDP growth used; results robust when using current real GDP growth.
- Extensions capture private behavior changes:
  - Equation [2] adds Teleworkability Index_i,c and its interaction with COVID_t.
  - Equation [3] adds E‑commerce Index_i,c and its interaction with COVID_t.
  - Segment‑specific results reported in Figure 8.

### Data, variables, and coverage (sample and key variables)
- Sample period for main estimations: 2016:Q1−2021:Q4.
- Key variable definitions and sources (labels preserved):
  - Change in COVID‑19 cases: Quarterly growth in COVID‑19 total cases per one million people (in percent). Source: https://ourworldindata.org/. Coverage: City‑ and country‑level.
  - CRE price growth: Quarterly change in asset value growth. Source: MSCI Real Estate and authors’ calculations. Coverage: City‑level.
  - Debt and contract relief: Index capturing public relief on financial obligations. Source: Oxford COVID-19 Government Response Tracker. Coverage: City‑ and country‑level.
  - Discretionary spending: Total discretionary fiscal response to the COVID‑19 pandemic (cumulative; percent of GDP). Source: Fiscal Monitor.
  - E‑commerce index: UNCTAD B2C E‑commerce Index. Coverage: Country‑level.
  - Expected real GDP growth: Projected quarterly change in real GDP one year ahead. Source: IMF’s WEO.
  - Financial Conditions Index: principal component of 11 variables (including real short‑term interest rates, equity prices, sovereign and corporate debt spreads, the exchange rate, and real house prices).
  - Public liquidity support: Equity, loans, and guarantees (cumulative; percent of GDP). Source: Fiscal Monitor.
  - Stringency Index: Indicator capturing stringency of containment measures. Source: Oxford COVID‑19 Government Response Tracker.
  - Teleworkability: Percentage of teleworkable jobs (Dingel and Neiman (2020)). Coverage: City‑level (Mixed).
  - Vaccination Policy Indicator: categorical [0-5]; Vaccination rate: cumulative vaccinated people per million. Sources: Oxford COVID‑19 Government Response Tracker; https://ourworldindata.org/.
  - Vacancy rate: all available property to total rental property (in percent). Source: MSCI Real Estate.
- Missing city‑level info proxied at metro‑ or country‑level; all control variables standardized for comparability.

### Conclusion and Policy Recommendations

### Main conclusions
- CRE prices varied widely across regions and segments during the COVID‑19 pandemic.
- Regions with more stringent containment measures and faster growth in COVID‑19 cases experienced larger CRE price declines on average.
- Larger fiscal policy support and easier financial conditions helped cushion the shock.
- Structural shifts accelerated by the pandemic—teleworking and e‑commerce—pose considerable uncertainty for the sector, especially for office and retail segments.
- The industrial segment gained from e‑commerce but may see moderated gains as in‑store activity recovers with vaccination.

### Financial stability concerns
- The CRE sector may face renewed pressure if global financial conditions tighten sharply amid monetary policy normalization by major central banks and if economic activity slows down.
- Interconnectedness of CRE with the broader financial system could pose financial stability risks.

### Recommended supervisory actions
- Continue vigilance by financial supervisors to monitor sector vulnerabilities to minimize potential financial stability risks.
- Consider stress testing exercises embedding large declines in commercial real estate prices to inform decisions regarding the adequacy of capital buffers for commercial real estate exposures.
- Review banks’ commercial real estate valuation assumptions and ensure that provisions are adequate.
- Where nonbank financial institutions are important CRE funding players, monitor their vulnerabilities and consider broadening the policy perimeter to mitigate system‑wide risks.

*Source: gfsnea2022002 - INTRODUCTION; 1. Overall Fiscal Stimulus; 1. Global Bust and Recovery (IMF Monetary and Capital Markets).*

### INTRODUCTION

### INTRODUCTION

### Pre‑pandemic trends and COVID‑19 reversal
- CRE prices rose annually at about 6 percent on average during 2009–19, with North America experiencing the steepest increase.
- At the onset of the pandemic CRE prices plummeted, particularly in Europe and North America, and in the retail segment.
- Aggregate CRE prices started to rebound in late 2020 and early 2021, but recovery across regions and segments has been uneven:
  - North America: aggregate CRE prices increased noticeably driven by a surge in residential and industrial property prices; retail and office recovery lagged.
  - Asia and Europe: average increases smaller than North America; office and retail performed relatively better in 2021.
- CRE transaction volumes fell by 20 percent to 70 percent across regions during the decline and may have limited further price declines.
- The sample used in the analysis covers quarterly CRE price data for 96 metropolitan cities from 31 major advanced and emerging market economies from 2016 to 2021.

### Dispersion in CRE prices: stylized facts and cross‑sectional patterns
- Dispersion during the pandemic:
  - The standard deviation of changes in aggregate CRE prices across cities was almost twice as large in the pandemic (2020:Q1–2021:Q4) than in the pre‑pandemic period (2016:Q1–2019:Q4).
  - Dispersion across CRE segments within cities was also much larger during the pandemic than before.
- City and regional examples:
  - Canada: aggregate CRE prices fell, on average, by 2 percent as the pandemic evolved in the first three quarters of 2020; within Canada, Regina recorded about a 4 percent decline (quarter‑over‑quarter average) while Toronto remained stable.
  - Global cities: Johannesburg recorded the largest drop (3 percent) during the first three quarters of 2020; Berlin and Oslo experienced the largest increases (of 3–5 percent, respectively). On average, CRE prices rose slightly (0.3 percent) in global cities during the early phase of the pandemic but declined by 0.7 percent elsewhere.
  - Retail segment: prices fell by about 7 percent in some cities (e.g., Baltimore, Glasgow, Minneapolis) in 2020:Q1–Q3 but increased in others (e.g., Austin, Kuala Lumpur, Seoul).
- Segment performance (first three quarters of 2020):
  - Retail: most severely hit; average quarterly decline about 2.5 percent in 2020:Q1–Q3.
  - Office: prices remained stable on average, though some cities saw declines between 2 percent to 6 percent.
  - Residential: prices increased by about 1 percent.
  - Industrial: prices increased by about 1.3 percent.
- Performance since end‑2020 (2020:Q4–2021:Q4):
  - Cities in Australia, New Zealand, and the United States showed a rebound in CRE prices in 2021, while prices continued to fall in several other cities (e.g., Canada, Ireland, South Africa).
  - Retail segment: average quarterly decline in 2020:Q4–2021:Q4 was 1.2 percent (smaller than the 2.5 percent decline in 2020:Q1–Q3).
  - Office sector: prices fell by 0.2 percent on average in 2020:Q4–2021:Q4, compared to a small increase of 0.1 percent during 2020:Q1–Q3.
  - Overall, about 77 percent of the cities in the sample experienced a decline in retail prices in 2020:Q4–2021:Q4 (as compared to 84 percent in 2020:Q1–Q3).
- Notable micro patterns:
  - Residential resilience in many areas contrasted with declines in densely populated central locations, consistent with household migration to less crowded suburban areas.

### Explaining CRE price changes during the pandemic
- Conceptual drivers:
  - Commercial property prices depend on current and expected net operating income growth and returns, which in turn depend on vacancy rates, economic activity, and financial market conditions.
  - Pandemic‑specific factors considered include containment measures, spread of the pandemic, policy support, and behavioral shifts (teleworking, e‑commerce).
  - Vacancy rates generally rose during the pandemic, especially where containment measures were more stringent.

- Containment measures ("The Great Shutdown"):
  - Regression evidence: cities with the most stringent containment measures (proxied by the Oxford stringency index) on average experienced a 1.5 percentage points larger decline in overall CRE prices than those without any restrictions, controlling for other macroeconomic and location‑specific factors.
  - Containment measures adversely affected all CRE segments, with the most pronounced quantitative effect on the residential segment (consistent with household migration away from dense urban areas).

- Pandemic spread and voluntary behavior:
  - Spread proxy: change in confirmed COVID‑19 cases.
  - A one standard deviation increase in the number of COVID‑19 cases (equivalent to a quarterly increase of about 200 percent) reduced prices in the retail segment by 0.3 percentage points.
  - The statistically significant effect of pandemic spread—controlling for mandatory containment measures—suggests voluntary social distancing also reduced demand and prices.
  - Vaccination effects:
    - Greater vaccine adoption (proxied by a vaccination policy indicator or vaccination rate) significantly supported CRE recovery, particularly in retail and industrial segments.
    - The vaccination policy indicator is categorical from 0 to 5; the vaccination rate is measured as vaccinated people per million. (Regression bars report effects such as extending vaccines’ access from 0 to 5, or a one standard deviation increase in vaccination rate; standard errors at 90 percent confidence intervals are reported.)

- Policy support:
  - Fiscal support:
    - Overall economic fiscal support during the pandemic is positively associated with CRE prices.
    - A one standard deviation increase in stimulus spending (equivalent to 9 percent of GDP) is associated with a 0.1 percentage point smaller quarterly decline in overall CRE prices.
    - Discretionary spending particularly helped retail, residential, and industrial segments.
    - Public liquidity support (equity injections, loans, guarantees) mostly benefitted retail and office segments.
    - Debt and contract relief to households is associated with significantly smaller price declines across CRE segments.
  - Monetary and financial conditions:
    - Financial conditions tightened sharply in the early phase of the pandemic but eased quickly after major central banks implemented massive monetary stimulus.
    - Easier financial conditions lifted CRE prices; the impact was more pronounced during the pandemic for retail, residential, and industrial segments.
    - A potential tightening of financial conditions as monetary policy normalizes could adversely impact CRE via higher financing costs and slower economic activity, warranting vigilance by financial supervisors to mitigate macro‑financial stability risks.

*Source: gfsnea2022002 - INTRODUCTION (gfsnea2022002 - INTRODUCTION)*

### 1. Overall Fiscal Stimulus

### 1. Overall Fiscal Stimulus

### Effect of fiscal stimulus, discretionary spending, liquidity support, and debt contract relief on CRE prices
- Panel definitions and magnitudes in the analysis:
  - Panel 1: effect of one standard deviation increase in fiscal spending to GDP on CRE price changes. Standard error bars indicate 90 percent confidence interval. 
  - Panel 2: effect of a one percent of GDP increase in discretionary spending (percentage points).
  - Panel 3: effect of a one standard deviation increase in total government equity injections, loans, and guarantees during the pandemic (about 6 percent of GDP) on the change in CRE prices.
  - Panel 4: effect of one standard deviation increase in the debt and contract relief index on the change in CRE prices.
- Sources used for these estimates: IMF Fiscal Monitor database; MSCI Real Estate; Oxford COVID-19 Government Response Tracker; and authors’ calculations.
- Statistical reporting note: all panels report standard error bars reflecting a 90 percent confidence interval.

### Key finding
- Larger fiscal policy support and easier financial conditions helped to cushion CRE price declines during the pandemic.

### Policy implications (fiscal / liquidity perspective)
- The documented cushioning role of fiscal policy and public liquidity support underscores the importance of targeted fiscal responses during severe real estate shocks.

---

### CRE Market Sentiment and Financial Conditions during the COVID-19 Pandemic
- Data and indicators:
  - Investor and occupier sentiment indicators are survey-based and measure the proportion reporting a rise minus those reporting a fall.
  - Financial conditions index impacts on CRE price growth are reported as the total effect of a one standard deviation increase in the financial conditions index during the pandemic period (sum of pre-pandemic marginal impact and additional post-pandemic impact).
  - Standard error bars reflect a 90 percent confidence interval.
- Empirical observation:
  - The impact of policy support during the pandemic is evident from the absence of wide-scale deleveraging in the CRE sector and by the lower level of distressed deals compared with earlier downturns (for example, the global financial crisis and the early 1990s recession).
- Contextual note:
  - Tighter or looser financial conditions and Fed interventions are highlighted as factors linked to CRE market sentiment and conditions.

---

### Behavioral Shifts

### Teleworking
- Survey-based magnitudes for the United States:
  - "42 percent of the labor force in the United States—accounting for two-thirds of economic activity—was working from home full time in the second quarter of 2020."
  - "About 26 percent was working onsite at the business premises" in the same period.
  - By contrast, "only 6 percent of those employed in the United Stated in 2019 worked primarily from home and about three-quarters had never worked from home (Coate 2021)."
- Empirical effects:
  - Cities with a higher share of teleworkable jobs—captured by Dingel and Neiman’s (2020) teleworkability index—experienced larger declines in office property prices, particularly in the initial phase of the pandemic.
  - Residential property prices were also lower during the pandemic in regions with higher teleworkability, consistent with relocation out of urban areas.
- Estimation details:
  - Panel 1 of Figure 8 reports the additional effect of a one standard deviation increase in the teleworkability index on quarterly office property price growth during 2020:Q1−2021:Q4 and in the initial phase (first three quarters of 2020), as well as impacts on residential price growth. Standard error bars reflect a 90 percent confidence interval.
  - The teleworkability index is cross-sectional and largely applied at country level for city analyses; the index is assumed not to have changed much over time.

### E-commerce
- Observed shifts:
  - "Sales in the United States jumped by 43 percent in 2020 relative to the year before to reach almost 15 percent of total retail trade (Brewster 2022)."
  - The shift toward e-commerce has been observed in other countries with variation across countries.
- Empirical effects:
  - A one standard deviation increase in the United Nations Conference on Trade Development e-commerce index is associated with:
    - "a 0.5 percentage point reduction in prices in the retail segment"
    - "a 0.9 percentage point increase in the industrial segment."
  - Panel 2 of Figure 8 shows how a one standard deviation increase in the UNCTAD e-commerce index affected price growth across sectors before the pandemic (2016:Q1−2019:Q4) and the additional effect after the pandemic (2020:Q1−2021:Q4). Standard error bars reflect a 90 percent confidence interval.
- Measurement caveat:
  - The e-commerce index is available only at country level and at an annual frequency and thus does not capture within-country variation.

### Implications of behavioral shifts
- Some CRE segments—particularly office and retail—may remain under pressure in the near to medium terms if teleworking and e-commerce trends persist.
- The industrial segment benefitted significantly from the rise in e-commerce during the pandemic but may see more moderate gains going forward as greater vaccine adoption encourages at least some return to in-store retail.
- Potential offset:
  - Downward pressure on prices could be partially offset if demand for specific properties picks up for alternative uses (for example, office-to-residential conversions; retail-to-warehouse/fulfillment-center conversions).

---

### Other Factors Affecting CRE Prices

### House prices and price momentum
- Commercial property prices are generally positively associated with house prices (Gyourko 2009).
- During the pandemic, variation in house prices increased across cities—particularly in Asia and Europe—which may have contributed to increased variation in CRE prices.
- Price momentum (lagged CRE price growth) is an important explanatory factor:
  - Cities that experienced higher price growth before the pandemic were less affected in the initial phase and subsequently experienced faster price growth, controlling for other factors.

### Cross-sectional and country heterogeneity
- Variation in containment measures, pandemic spread, fiscal support, teleworkability, and e-commerce potential all explain cross-sectional differences in CRE price outcomes.
- Increased heterogeneity across countries in the pace of economic recovery contributed to large variation observed in retail prices, given the tight link between expected future economic activity and projected net operating income for investors.
- Empirical dispersion measures:
  - The standard deviation of quarterly growth in real house prices in the sample increased by a factor of approximately 1.5 between the pre-pandemic and pandemic periods.

---

### Conclusion and Policy Recommendations

### Main conclusions
- CRE prices varied widely across regions and segments during the COVID-19 pandemic.
- Regions with more stringent containment measures and faster growth in COVID-19 cases experienced larger CRE price declines on average.
- Larger fiscal policy support and easier financial conditions helped cushion the shock.
- Structural shifts accelerated by the pandemic—teleworking and e-commerce—pose considerable uncertainty for the sector, especially for office and retail segments.
- The industrial segment gained from e-commerce but may see moderated gains as in-store activity recovers with vaccination.

### Financial stability concerns
- The CRE sector may face renewed pressure if global financial conditions tighten sharply amid monetary policy normalization by major central banks and if economic activity slows down.
- Given the interconnectedness of CRE with the broader financial system, these dynamics could pose financial stability risks.

### Recommended supervisory actions
- Continue vigilance by financial supervisors to monitor sector vulnerabilities to minimize potential financial stability risks.
- Consider stress testing exercises embedding large declines in commercial real estate prices to inform decisions regarding the adequacy of capital buffers for commercial real estate exposures.
- Review banks’ commercial real estate valuation assumptions and ensure that provisions are adequate.
- Where nonbank financial institutions are important CRE funding players, monitor their vulnerabilities and consider broadening the policy perimeter to mitigate system-wide risks.

*Source: gfsnea2022002 - 1. Overall Fiscal Stimulus (IMF Monetary and Capital Markets).*

### 1.  Global Bust and Recovery

### 1. Global Bust and Recovery

### CRE price dynamics and vacancy patterns (figures and descriptive notes)
- Panels compare the Global Financial Crisis (GFC) and COVID-19 episodes:
  - CRE price series use MSCI Real Estate and authors’ calculations.
  - In panel 1 (bust), t=0 indicates the precrisis peak in global CRE price (average across all segments and regions).
  - CRE segments shown: All, Retail, Office, Residential, Industrial.
- Figure A3 — Commercial Real Estate Vacancy Rates Pre- and Post-Pandemic:
  - Panel 1: Vacancy Rates, 2019:Q1–2021Q4 (Percent) — overall CRE vacancy rate levels averaged over the sample.
  - Panel 2: Vacancy Rates and Stringency Index (Percent) — effect of a one unit increase in the stringency and financial condition indices during the pandemic on the overall vacancy rate with a 90 percent confidence interval, controlling for a lagged vacancy rate, city and quarter fixed effects, and a dummy for the COVID-19 pandemic period. Standard errors clustered at the city level. Sample period for estimation: 2016:Q1-2021:Q4.
- Figure A4 — House Price Growth and Distance from Center, 2020:Q1–2021:Q4:
  - Shows relationship between Zillow’s all homes price growth (quarterly average) for 154 counties in the United States and the distance from the central county in the same metropolitan area for 2020:Q1 to 2021:Q4.
- Figure A5 — CRE Distressed Transactions (Percent of total transaction volume):
  - Sources: MSCI Real Estate; Real Capital Analytics; RICS; and authors’ calculations.
  - Note: Americas = North and Latin America; EMEA = Europe, the Middle East, and Africa.

### Empirical framework (baseline and extensions)
- Baseline specification (equation [1]) — dependent variable: quarterly real commercial property price growth (in percent). Index notation: i (city), c (country), t (quarter).
- Baseline regressors include:
  - Lagged CRE price growth: CRE price growth_i,c,t−1
  - Financial Condition Index_c,t
  - Stringency Index_i,c,t
  - Change in COVID cases_c,t (quarterly percentage change in confirmed COVID-19 cases)
  - Fiscal Measures_c,t (additional economic stimulus spending as percent of GDP reported by the government in each quarter during the pandemic)
  - Real GDP growth_c,t+4 (projected one-year ahead real GDP growth from IMF WEO)
  - Change in House Prices_c,t (quarterly growth in house prices)
  - COVID_t (indicator = 1 in 2020:Q1−2021:Q4, and zero otherwise)
  - Interactions: X_i,c,t × COVID_t for non-pandemic-specific variables
  - Quarter fixed effects D_q,t and city fixed effects α_i
  - Error term ε_i,c,t
- Estimation notes:
  - Lagged dependent variable included to capture momentum effects.
  - Nickell bias (approx equal to 1/T) acknowledged; robustness checks estimate eq. (1) with country-level fixed effects and without the lagged dependent variable; results remain robust.
  - Values above the 99th percentile excluded from sample for certain variables to avoid bias from large outliers.
  - Projected future real GDP growth used; results robust when using current real GDP growth.
- Extensions to capture private behavior changes:
  - Equation [2] adds Teleworkability Index_i,c and its interaction with COVID_t.
  - Equation [3] adds E‑commerce Index_i,c and its interaction with COVID_t.
  - Estimation for overall CRE prices reported in Table 2, columns 6-8; segment-specific results in Figure 8. Full results available upon request.

### Data, variables, and coverage (Table 1 content)
- Sample period for main estimations: 2016:Q1−2021:Q4.
- Data sources and variable descriptions (exact labels preserved):
  - Change in COVID-19 cases: Quarterly growth in COVID-19 total cases per one million people (in percent). Source: https://ourworldindata.org/. Coverage: City- and country-level.
  - CRE price growth: Quarterly change in asset value growth. Source: MSCI Real Estate and authors’ calculations. Coverage: City-level.
  - Debt and contract relief: Index capturing public relief on financial obligations (loan moratoria, banning of evictions, delayed utility payments). Source: Oxford COVID-19 Government Response Tracker. Coverage: City- and country-level.
  - Discretionary spending: Total discretionary fiscal response to the COVID-19 pandemic (cumulative; percent of GDP). Source: Fiscal Monitor. Coverage: City- and country-level.
  - E-commerce index: UNCTAD B2C E-commerce Index. Netcraft retrieved from World Bank. Coverage: Country-level.
  - Expected real GDP growth: Projected quarterly change in real GDP one year ahead. Source: IMF’s WEO database. Coverage: Country-level.
  - Financial Conditions Index: Based on principal component analysis of 11 variables (including real short-term interest rates, equity prices, sovereign and corporate debt spreads, the exchange rate, and real house prices). See Online Annex 1.1 of the October 2018 Global Financial Stability Report. Coverage: Country-level.
  - Fiscal support measure: Cumulative additional economic stimulus spending (percent of GDP). Source: Oxford COVID-19 Government Response Tracker. Coverage: City- and country-level.
  - House price growth: Quarterly change in real house prices (in percent). Source: Bank of International Settlements. Coverage: City- and country-level.
  - Public liquidity support: Equity, loans, and guarantees extended during the pandemic (cumulative; percent of GDP). Source: Fiscal Monitor. Coverage: Country-level.
  - Stringency Index: Indicator capturing stringency of containment measures (higher values = greater stringency). Source: Oxford COVID-19 Government Response Tracker. Coverage: City- and country-level.
  - Teleworkability: Percentage of teleworkable jobs in an economy (Dingel and Neiman (2020)). Coverage: City-level (Mixed).
  - US county-level data: House price growth, population density, distance from central county. Sources: Zillow; NBER. Coverage: County-level.
  - Vacancy rate: All available property to total rental property (in percent). Source: MSCI Real Estate. Coverage: City-level.
  - Vaccination Policy Indicator: Categorical variable [0-5] capturing coverage of vaccine delivery policies. Source: Oxford COVID-19 Government Response Tracker. Coverage: City-level (Mixed).
  - Vaccination rate: Cumulative number of vaccines weighted by population. Source: https://ourworldindata.org/. Coverage: City-level (Mixed).
- Notes:
  - If complete or partial city-level information not available, missing data proxied at metro- or country-level; indicated as “Country-level” or “City-level (Mixed)”.
  - All control variables in the regressions are standardized for comparability.

*Source: gfsnea2022002 - 1. Global Bust and Recovery*

---


_Source: https://www.imf.org/-/media/files/publications/gfs-notes/2022/english/gfsnea2022002.pdf_