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Introduction

• Global housing crisis in 2025 has been a large topic of concern
• Definition of key terms:
  • Dwellings: Buildings that are primarily designed for long-term residential purposes
  • Housing stress: housing costs that exceed 30% of the average income for low-income households
  • Housing affordability: Relationship between housing costs and household income, can vary between locations
  • Affordable housing: housing provided at discounted market rates, typically targeted towards households that are part of the low-middle income class.
  • Social housing: Government or community managed housing typically provided to those who are unable to access affordable, adequate housing in the market.
  • Overcrowding: The number of residents that live within the dwelling exceeding the designed capacity due to affordability challenges or housing shortages.

Introduction cont.

• Key findings from Australia’s National Housing Supply and Affordability Council include:
  • Rises in housing prices and rent outpaced the rise in the median household income
  • Housing affordability deteriorating
  • Supply of housing is near its lowest levels in a decade
• Key findings from CBRE Investment Management on the state of housing in the United States include:
  • Home prices reaching historical highs
  • Income required to buy a home has doubled since 2019
  • Key problem is the difference in supply and demand of houses in the US
    • Not enough housing, wrong types of houses being supplied for current demand
  • Regulatory barriers include:
    • Local zoning regulations
    • Higher cost of capital, goods and labour
    • Rising interest rates, which significantly decreased development activity despite strong consumer demand for housing

Problem Statement

• “How do the different variables (i.e. property prices, furnishing statuses, salaries, etc.) interact with the purchasing decisions of buyers?
• Statistics will be employed to:
  • Explore the relationship between average property prices across different countries
  • Explore the relationship between monthly salary and property prices
  • Explore the relationship between property price and the number of previous owners
  • Explore the relationship between furnishing status and buyer decisions

Data

• Explain how you collected your data: I went to Kaggle.com and searched for a recently updated housing dataset.
• Open Data Reference: https://www.kaggle.com/datasets/mohankrishnathalla/global-house-purchase-decision-dataset
• List of important variables in the dataset:
  • Property_type describes the type of the developed property, such as a studio, an apartment, a townhouse, etc.
  • Furnishing_status describes the furninshing condition of the developed property, such as unfurnished or furnished
  • The price describes the price of the property in the local currency
  • Monthly_expenses describes the monthly expenses of the customer that is looking to buy the property
  • Customer_salary describes the monthly salary of the customer looking to buy the property

Data cont.

• Preprocessing done to the data set:
  • Converted the decision variable into a factor, with 0 being No and 1 being Yes
    • This helps with plotting, as well as treating purchase decision as a numerical outcome
  • Converted all dollar values from local currency to USD for easier comparison

ds <- read.csv("global_house_purchase_dataset.csv")
ds$decision <- ds$decision %>% factor(levels = c(0,1), labels = c("No", "Yes"))
ds$furnishing_status <- ds$furnishing_status %>% factor(levels = c("Unfurnished", "Semi-Furnished", "Fully-Furnished"), ordered=TRUE)
ds_filtered <- subset(ds, !country %in% c("Brazil", "China", "India", "Germany","Japan", "South Africa", "UAE"))
currency_pairs <- c("AUDUSD=X", "CADUSD=X", "EURUSD=X", "SGDUSD=X", "GBPUSD=X")

exchange_data <- getQuote(currency_pairs)
exchange_rates <- exchange_data$Last

currency_codes <- c("AUD", "CAD","EUR", "SGD", "GBP")
names(exchange_rates) <- currency_codes

exchange_rates <- c("USD" = 1, exchange_rates)
country_currency <- c("Australia" = "AUD", "Canada" = "CAD", "France" = "EUR","Singapore" = "SGD", "UK" = "GBP", "USA" = "USD")
ds_filtered$currency <- country_currency[ds_filtered$country]
ds_filtered$price_usd <- ds_filtered$price * exchange_rates[ds_filtered$currency]
ds_filtered$salary_usd <- ds_filtered$customer_salary*exchange_rates[ds_filtered$currency]

Descriptive Statistics and Visualisations

• Summarise the important variables in your investigation.
• Use visualisation to highlight interesting features of the data and tell the overall story.

country_summary <- ds_filtered %>%
  group_by(country) %>%
  summarise(avg_price_usd = mean(price_usd, na.rm = TRUE),
    median_price_usd = median(price_usd, na.rm = TRUE),
    avg_salary_usd = mean(salary_usd, na.rm = TRUE))
knitr::kable(country_summary)

Dessriptive Statistics and Visualisations cont.

• The histogram below shows the distribution of the property sizes that are within the data set.
• The roughly uniform distribution could indicate that the data set was pre-processed by the creator before it was published online.

ggplot(ds_filtered, aes(x = property_size_sqft)) +
  geom_histogram(binwidth = 100, fill = "steelblue", color = "white") +
  labs(title = "Distribution of Property Size (sqft)",
       x = "Property Size (sqft)",y = "Frequency") +
  theme_minimal() + theme(plot.title = element_text(hjust = 0.5))

Descriptive Statistics and Visualisations cont.

• The boxplot below describes the prices of properties across the different countries being analysed in the data set.
• Looking at the boxplot, the median property price (solid black line) is higher in countries like Singapore, the UK, US, and France compared to countries like Australia and Canada.

# Boxplot: Price comparison by Country
ggplot(ds_filtered, aes(x = country, y = price_usd, fill = country)) +
  geom_boxplot(fill = "white", color = "black") +
  xlab("Country") +  ylab("Property Price (USD)") +  theme_classic() +
  labs(title = "Boxplot of Property Prices by Country") +
  scale_y_continuous(labels = scales::comma) +  
  theme(legend.position = "none", axis.text.x = element_text(angle = 90, size = 10),
    axis.title.x = element_text(size = 12),
    axis.title.y = element_text(size = 12),
    plot.title = element_text(size = 16))

Regression analysis

# Correlation test between previous owners and price of property
cor.test(ds_filtered$previous_owners, ds_filtered$price_usd)

## 
##  Pearson's product-moment correlation
## 
## data:  ds_filtered$previous_owners and ds_filtered$price_usd
## t = -0.17441, df = 92441, p-value = 0.8615
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.007019945  0.005872699
## sample estimates:
##           cor 
## -0.0005736468

confint

## function (object, parm, level = 0.95, ...) 
## UseMethod("confint")
## <bytecode: 0x000001e9a3fb9118>
## <environment: namespace:stats>

• The correlation coefficient calculated is -0.0005,

# Linear regression model to analyse relationship between previous owner and property price
model1<-lm(previous_owners ~ price_usd, data = ds_filtered)
model1 %>% summary()

## 
## Call:
## lm(formula = previous_owners ~ price_usd, data = ds_filtered)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.01115 -2.00845 -0.00961  1.98967  2.99319 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.011e+00  1.248e-02 241.286   <2e-16 ***
## price_usd   -1.373e-09  7.873e-09  -0.174    0.862    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2 on 92441 degrees of freedom
## Multiple R-squared:  3.291e-07,  Adjusted R-squared:  -1.049e-05 
## F-statistic: 0.03042 on 1 and 92441 DF,  p-value: 0.8615

Regression analysis cont.

# Correlation test between buyer salary and price of property
cor.test(ds_filtered$salary_usd, ds_filtered$price_usd)

## 
##  Pearson's product-moment correlation
## 
## data:  ds_filtered$salary_usd and ds_filtered$price_usd
## t = 52.626, df = 92441, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1642857 0.1768033
## sample estimates:
##       cor 
## 0.1705514

# Linear regression model to analyse relationship buyer salary and property price
model2<-lm(price_usd ~ salary_usd, data = ds_filtered)
model2 %>% summary()

## 
## Call:
## lm(formula = price_usd ~ salary_usd, data = ds_filtered)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1524283  -670031  -162244   628289  2107550 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.091e+06  5.573e+03  195.72   <2e-16 ***
## salary_usd  4.941e+00  9.388e-02   52.63   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 823400 on 92441 degrees of freedom
## Multiple R-squared:  0.02909,    Adjusted R-squared:  0.02908 
## F-statistic:  2769 on 1 and 92441 DF,  p-value: < 2.2e-16

Regression analysis cont.

aov_model <- aov(price_usd ~ country, data=ds_filtered)
summary(aov_model)

##                Df    Sum Sq   Mean Sq F value Pr(>F)    
## country         5 1.767e+16 3.533e+15    6967 <2e-16 ***
## Residuals   92437 4.688e+16 5.072e+11                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

TukeyHSD(aov_model)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = price_usd ~ country, data = ds_filtered)
## 
## $country
##                           diff        lwr        upr     p adj
## Canada-Australia     121664.72   98552.76  144776.68 0.0000000
## France-Australia     886081.12  863053.36  909108.89 0.0000000
## Singapore-Australia 1052150.47 1028991.98 1075308.97 0.0000000
## UK-Australia        1042025.53 1018918.08 1065132.98 0.0000000
## USA-Australia        927145.35  903988.00  950302.70 0.0000000
## France-Canada        764416.40  741373.22  787459.58 0.0000000
## Singapore-Canada     930485.75  907311.94  953659.57 0.0000000
## UK-Canada            920360.81  897238.00  943483.62 0.0000000
## USA-Canada           805480.63  782307.95  828653.30 0.0000000
## Singapore-France     166069.35  142979.50  189159.20 0.0000000
## UK-France            155944.41  132905.74  178983.07 0.0000000
## USA-France            41064.23   17975.52   64152.93 0.0000060
## UK-Singapore         -10124.94  -33294.27   13044.38 0.8144291
## USA-Singapore       -125005.12 -148224.21 -101786.04 0.0000000
## USA-UK              -114880.18 -138048.36  -91712.00 0.0000000

Categorical association

• Statement: Does furnishing status influence the buying decision of a given property?
• Null hypothesis: There is no association between furnishing status and the buying decision
• Alternative: There is an association between the furnishing status and buying decision

furnishing_decision_table <- table(ds_filtered$furnishing_status, ds_filtered$decision)
chi_result <- chisq.test(furnishing_decision_table)
chi_result

## 
##  Pearson's Chi-squared test
## 
## data:  furnishing_decision_table
## X-squared = 4.81, df = 2, p-value = 0.09026

• p-value: 0.09026 (> 0.05) therefore we fail to reject the null hypothesis
• Conclusion: Results of chi-squared tests are not statistically significant. There is therefore not enough evidence to conclude that furnishing status influences the buying decision of a property.

Categorical association cont.

• Statement: Do buyers in certain countries have higher purchase rates than others?
• Null hypothesis: There is no association between a buyer’s country and their purhase decisions
• Alternative: There is an association

country_decision_table <- table(ds_filtered$country, ds_filtered$decision)
chi_result <- chisq.test(country_decision_table)
chi_result

## 
##  Pearson's Chi-squared test
## 
## data:  country_decision_table
## X-squared = 487.55, df = 5, p-value < 2.2e-16

• p-value: 2.2e-16 (< 0.05), therefore we reject the null hypothesis
• Conclusion: Results of the chi-squared tests are statistically significant. There is therefore enough evidence to conclude that buyer’s purchase rates differ greatly by country.

Categorical association cont.

• Statement: Are certain property types more likely to be furnished or not?
• Null hypothesis: There is no association between the furnishing status and property types
• Alternative: There is an association

property_furnishing_table <- table(ds_filtered$property_type, ds_filtered$furnishing_status)
chi_result <- chisq.test(property_furnishing_table)
chi_result

## 
##  Pearson's Chi-squared test
## 
## data:  property_furnishing_table
## X-squared = 7.8341, df = 10, p-value = 0.645

• p-value: 0.645 (> 0.05), fail to reject the null hypothesis
• Conclusion: Results of the chi-squared tests are not statistically significant. There is therefore not enough evidence to prove that a given property type has a higher chance of being furnished over another.

Discussion

• Discuss the major findings of your investigation
• Discuss any strengths and limitations.
• Propose directions for future investigations.
• This is a good place to re-state your findings as a final conclusion. What is the one take home message the reader should leave with?
• Your final conclusion needs to be very clear.

References

• Provide a list of any references you use in the presentation.
• https://www.cbreim.com/insights/articles/digging-out-of-the-us-housing-affordability-crisis
• https://nhsac.gov.au/reports-and-submissions/state-housing-system-2025