- Introduction
- Data Exploration
- Exploratory Data Analysis
- Predicting HDB apartment resale prices for the years of 2010 - 2022
- Conclusions
As a millenial growing up in Singapore and starting out in my first job, one of my life’s goals is to own a house on this little red dot with my life partner (read: wife).
Here in Singapore, there are generally two ways to do this:
- Public
Housing
- Brand new Built-to-Order (BTO) Flats (or apartments)
- Resale Flats
- Private Housing (e.g., condominiums, landed property, etc.)
Whilst BTOs tend to be the choice of most young Singaporean couples, my current and potential future situation may push my partner and I to look towards resale flats to meet our housing needs. As such, I set forth on this project that will focus on resale flats under the Housing & Development Board (HDB). I hope to uncover insights into the relationships between variables related to resale flat prices and whether the data can be fitted into a model that may give an idea of future prices.
This project therefore seeks to analyse the prices of Singapore’s HDB Resale Flats for the years of 1990 - 2022. Data was obtained from Kaggle, (1) and (2), which were sourced from GovTech’s open data portal, data.gov.sg.
First, we will examine the data to understand its structure and contents.
## # A tibble: 6 × 10
## month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ resal…⁷
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 1990-01 ANG MO … 1 ROOM 309 ANG MO… 10 TO … 31 IMPROV… 1977 9000
## 2 1990-01 ANG MO … 1 ROOM 309 ANG MO… 04 TO … 31 IMPROV… 1977 6000
## 3 1990-01 ANG MO … 1 ROOM 309 ANG MO… 10 TO … 31 IMPROV… 1977 8000
## 4 1990-01 ANG MO … 1 ROOM 309 ANG MO… 07 TO … 31 IMPROV… 1977 6000
## 5 1990-01 ANG MO … 3 ROOM 216 ANG MO… 04 TO … 73 NEW GE… 1976 47200
## 6 1990-01 ANG MO … 3 ROOM 211 ANG MO… 01 TO … 67 NEW GE… 1977 46000
## # … with abbreviated variable names ¹flat_type, ²street_name, ³storey_range,
## # ⁴floor_area_sqm, ⁵flat_model, ⁶lease_commence_date, ⁷resale_price
## # A tibble: 6 × 10
## month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ resal…⁷
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 2000-01 ANG MO … 3 ROOM 170 ANG MO… 07 TO … 69 Improv… 1986 147000
## 2 2000-01 ANG MO … 3 ROOM 174 ANG MO… 04 TO … 61 Improv… 1986 144000
## 3 2000-01 ANG MO … 3 ROOM 216 ANG MO… 07 TO … 73 New Ge… 1976 159000
## 4 2000-01 ANG MO … 3 ROOM 215 ANG MO… 07 TO … 73 New Ge… 1976 167000
## 5 2000-01 ANG MO … 3 ROOM 218 ANG MO… 07 TO … 67 New Ge… 1976 163000
## 6 2000-01 ANG MO … 3 ROOM 320 ANG MO… 04 TO … 73 New Ge… 1977 157000
## # … with abbreviated variable names ¹flat_type, ²street_name, ³storey_range,
## # ⁴floor_area_sqm, ⁵flat_model, ⁶lease_commence_date, ⁷resale_price
## # A tibble: 6 × 10
## month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ resal…⁷
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 2012-03 ANG MO … 2 ROOM 172 ANG MO… 06 TO … 45 Improv… 1986 250000
## 2 2012-03 ANG MO … 2 ROOM 510 ANG MO… 01 TO … 44 Improv… 1980 265000
## 3 2012-03 ANG MO … 3 ROOM 610 ANG MO… 06 TO … 68 New Ge… 1980 315000
## 4 2012-03 ANG MO … 3 ROOM 474 ANG MO… 01 TO … 67 New Ge… 1984 320000
## 5 2012-03 ANG MO … 3 ROOM 604 ANG MO… 06 TO … 67 New Ge… 1980 321000
## 6 2012-03 ANG MO … 3 ROOM 154 ANG MO… 01 TO … 68 New Ge… 1981 321000
## # … with abbreviated variable names ¹flat_type, ²street_name, ³storey_range,
## # ⁴floor_area_sqm, ⁵flat_model, ⁶lease_commence_date, ⁷resale_price
## # A tibble: 6 × 11
## month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ remai…⁷
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 2015-01 ANG MO … 3 ROOM 174 ANG MO… 07 TO … 60 Improv… 1986 70
## 2 2015-01 ANG MO … 3 ROOM 541 ANG MO… 01 TO … 68 New Ge… 1981 65
## 3 2015-01 ANG MO … 3 ROOM 163 ANG MO… 01 TO … 69 New Ge… 1980 64
## 4 2015-01 ANG MO … 3 ROOM 446 ANG MO… 01 TO … 68 New Ge… 1979 63
## 5 2015-01 ANG MO … 3 ROOM 557 ANG MO… 07 TO … 68 New Ge… 1980 64
## 6 2015-01 ANG MO … 3 ROOM 603 ANG MO… 07 TO … 67 New Ge… 1980 64
## # … with 1 more variable: resale_price <dbl>, and abbreviated variable names
## # ¹flat_type, ²street_name, ³storey_range, ⁴floor_area_sqm, ⁵flat_model,
## # ⁶lease_commence_date, ⁷remaining_lease
## # A tibble: 6 × 11
## month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ remai…⁷
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 2017-01 ANG MO … 2 ROOM 406 ANG MO… 10 TO … 44 Improv… 1979 61 yea…
## 2 2017-01 ANG MO … 3 ROOM 108 ANG MO… 01 TO … 67 New Ge… 1978 60 yea…
## 3 2017-01 ANG MO … 3 ROOM 602 ANG MO… 01 TO … 67 New Ge… 1980 62 yea…
## 4 2017-01 ANG MO … 3 ROOM 465 ANG MO… 04 TO … 68 New Ge… 1980 62 yea…
## 5 2017-01 ANG MO … 3 ROOM 601 ANG MO… 01 TO … 67 New Ge… 1980 62 yea…
## 6 2017-01 ANG MO … 3 ROOM 150 ANG MO… 01 TO … 68 New Ge… 1981 63 yea…
## # … with 1 more variable: resale_price <dbl>, and abbreviated variable names
## # ¹flat_type, ²street_name, ³storey_range, ⁴floor_area_sqm, ⁵flat_model,
## # ⁶lease_commence_date, ⁷remaining_lease
From these, we see that the data structure is generally similar. The
only difference being that the “Jan 2015 - Dec 2016” and “Jan 2017
onwards” data consists of an extra column remaining_lease, indicating
the remaining years of lease for the houses in the datasets. For the
purpose of this project then, we will be dropping this column to
maintain data uniformity.
Now that the data is similar, we can combine the datasets together.
Looking into the dataset, we see that the storey_range variable has
varied values, which we will be renaming into a new variable called
storeys.
However, before we create the new variable, it would be helpful to understand the range of storey ranges among the resale flats and create a new variable to categorise them.
Even in 2022, very few resale flats have more than 12 to 15 floors, hence, everything above the 9th floor will be tagged as “high”.
Now that the data is cleaned, we can do some quick exploratory analyses.
First, we know that generally, HDB flat prices tend to vary by storey levels. We can visualise this layman assumption here:
Although the variation and range of resale flat prices are relatively the same, the median prices for each of the storey categories seem to fit this assumption. That is, HDB flats on the lower levels generally tend to cost less than those on the higher levels.
One possible explanation for near similar variations and ranges could be the scope of the dataset, which covers approximately 30 years’ worth of data. Furthermore, the above chart does not take into account the HDB town, which, as most Singaporeans know, also influences the prices of flats.
Let’s examine the relationship between HDB towns and prices next:
It is therefore evident that HDB resale flat prices do vary by their towns.
At this point, as mentioned above, the range of data is still massive with about 871,671 observations. For ease of and more focused analyses, we will narrow down the dataset by three conditions:
- Apartments from 2010 onwards (because we want newer houses and buildings)
- Apartments of at least 4-room and not multi-generation (so that we have a flat that is big enough to house at least 2 children)
- Apartments located on “mid” or “high” storeys (as we simply prefer being higher up and for the higher resale value over time)
## # A tibble: 6 × 13
## month year town flat_…¹ flat_…² block stree…³ store…⁴ storeys floor…⁵
## <chr> <int> <chr> <chr> <fct> <chr> <chr> <chr> <fct> <dbl>
## 1 2010-01 2010 ANG MO KIO 4 ROOM 4-room 414 ANG MO… 07 TO … mid 92
## 2 2010-01 2010 ANG MO KIO 4 ROOM 4-room 472 ANG MO… 13 TO … high 92
## 3 2010-01 2010 ANG MO KIO 4 ROOM 4-room 624 ANG MO… 10 TO … high 93
## 4 2010-01 2010 ANG MO KIO 4 ROOM 4-room 643 ANG MO… 07 TO … mid 100
## 5 2010-01 2010 ANG MO KIO 5 ROOM 5-room 418 ANG MO… 07 TO … mid 148
## 6 2010-01 2010 ANG MO KIO 5 ROOM 5-room 545 ANG MO… 13 TO … high 118
## # … with 3 more variables: flat_model <chr>, lease_commence_date <dbl>,
## # resale_price <dbl>, and abbreviated variable names ¹flat_type, ²flat_type2,
## # ³street_name, ⁴storey_range, ⁵floor_area_sqm
This gives us a dataset with 38,695 observations - something much easier to work with and relatively more reflective of current trends, considering the recency of the data.
Here, we repeat the analyses from the previous section using the filtered dataset.
We also add another analysis:
We can make three observations here:
For HDB Resale Flats sold from 2010 onwards,
- Apartments on higher floors (i.e., at least 11th floor) tend to be priced SGD 40,000 more than those on middle floors (i.e., 6th to 10th floors)
- Apartments in the areas nearer to the Central Business District (CBD) area of Singapore tend to be priced more than those further away, with those in mature estates priced higher than those in non-mature estates (see here for a list of such estates)
- Apartments with larger flat types tend to be priced higher than relatively smaller ones
Another interesting observation is with the spread of resale prices by HDB towns.
Let’s take a look at both the unfiltered (left) and filtered (right) data visualisations side by side:
We see that when comparing the range of HDB resale flat prices for 1990 to 2022 vs. for 2010 to 2022, the second observation above does not hold. The simple explanation here is that, many estates (e.g., Punggol, Sengkang, Central Area) did not have many HDB flats built prior to 2010 (or even 2000).
As Singapore developed further, with land scarcity increasing, more HDB flats needed to be built closer to the CBD to meet the demands of working professionals whose offices were located there. As such, even mature estates such as Queenstown and Bukit Merah, being some of the oldest estates in Singapore, with their newly developed HDB flats in the early 2000s, started seeing increased resale prices due to their proximity to the CBD. Furthermore, with the development of Sengkang and Punggol in line with more recent trends in HDB resale flat prices, these newer flats came out on top in terms of resale prices when being compared to flats that were built long before 2010, or even 2000.
This brings us to another variable that we have not investigated: the lease commencement date. The years variable used thus far were the years that the HDB flat was put on the resale market. As such, the lease commencement date would give an indicator of the age of the HDB flat.
Let’s take a look at the visualisation here:
The plot above shows that there is a general upward trend for newer HDB flats, which by common logic, does make sense. The only “outlier” in this plot is that of HDB resale flats in Sembawang, which saw a downward trend followed by a spike in the late 2010s. This could have been due to recent urban developments in the Canberra area, located near Sembawang.
Nevertheless, when looking at the data as a whole, it still shows that the resale prices increased for newer HDB resale flats (rho = 0.251, p < 0,05).
##
## Spearman's rank correlation rho
##
## data: filtered_data$lease_commence_date and filtered_data$median_price_by_lease
## S = 7.2359e+12, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2506549
Finally, we can also examine whether the HDB resale prices follow a pattern or trend over time.
- Apartments on higher floors tend to be priced higher (at least SGD 40,000 more between high and middle floors)
- Apartments nearer to the CBD tend to be priced higher than those futher away
- Apartments in mature estates tend to be priced higher than those in non-mature estates
- Apartments with larger flat types tend to be priced higher than relatively smaller ones
- Newer apartments tend to be priced higher than relatively older ones (i.e., with shorter remaining lease years)
- Resale prices generally dip at the end of the year and then dip again at around mid-year
- Executive apartment resale prices are more volatile (variable) than 4-room and 5-room apartment resale prices
In predicting HDB resale prices, the exploratory analyses revealed some key variables:
- Flat-type (e.g., 4-room, 5-room, Executive)
- This variable is synonymous with the floor area of the house, which
is represented by the
floor_area_sqmvariable
- This variable is synonymous with the floor area of the house, which
is represented by the
- Month of sale
- Year of lease commencement (i.e., indicating the remaining length of the house lease at point of sale)
- Storeys (e.g., Mid or High floors)
- Region (i.e., indicated by Town, which indicates proximity to the CBD)
Out of these variables, we will need to create or calculate new variables for the length of housing lease at point of sale, for the housing region and for the month of sale.
For the town groupings by region, we will use the information found here.
As such, the feature variables we will be using to predict HDB resale prices would be:
- Flat Type (
flat_type2) - Floor area in sqm (
floor_area_sqm) - Remaining lease length in years (
remaining_lease_length) - Region (
region) - Floor level (
storeys) - Month of sale (
month_2)
Since flat type and floor area are synonymous, we can conduct an ANOVA test on both variables to test for feature dependence and consider whether we should use both variables as features in a model or only one.
For this ANOVA test, we will set our alpha level to 0.1 and the null hypothesis that there is no difference between the means of the floor area for the flat types.
## Analysis of Variance Table
##
## Response: floor_area_sqm
## Df Sum Sq Mean Sq F value Pr(>F)
## flat_type2 2 9370466 4685233 77207 < 2.2e-16 ***
## Residuals 38692 2347999 61
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The ANOVA test showed that the p-value was less than 0.001 and so we can reject the null hypothesis; concluding that there is a mean difference between the floor area of flat types.
To determine which pairs of flat types had differences, we can conduct a series pairwise t-test with Bonferroni correction. We will set alpha to 0.1 for this test.
##
## Pairwise comparisons using t tests with pooled SD
##
## data: filtered_data$floor_area_sqm and filtered_data$flat_type2
##
## 4-room 5-room
## 5-room <2e-16 -
## Executive <2e-16 <2e-16
##
## P value adjustment method: bonferroni
The pairwise t-tests showed p-values of less than 0.001 for all possible pairs, which means that we can reject all null hypotheses that there were no differences in mean floor areas between flat types. As such, we will be keeping both variables for predicting HDB resale prices.
After exploring our data and gaining some insights into the trends and factors possibly influencing resale prices, we will be modelling the prices to predict them.
For these linear regression models, the alpha level will be set at 0.1.
Three models were fitted:
- A model with all the feature variables listed above (aka the Full model)
- A model without the flat type variable
- A model without the month of year variable
## MODEL INFO:
## Observations: 38695
## Dependent Variable: resale_price
## Type: Linear regression
##
## MODEL FIT:
## χ²(20) = 459128647515962.31, p = 0.00
## Pseudo-R² (Cragg-Uhler) = 0.59
## Pseudo-R² (McFadden) = 0.03
## AIC = 993973.99, BIC = 994162.39
##
## Standard errors: MLE
## --------------------------------------------------------------------
## Est. S.E. t val. p
## ---------------------------- ------------ --------- --------- ------
## (Intercept) 192393.54 7991.52 24.07 0.00
## flat_type25-room 42930.32 1748.21 24.56 0.00
## flat_type2Executive 101234.29 3442.75 29.41 0.00
## floor_area_sqm 2092.59 63.74 32.83 0.00
## remaining_lease_length 3070.20 47.70 64.36 0.00
## regioneast -184964.82 1745.95 -105.94 0.00
## regionnorth -282939.00 1644.10 -172.09 0.00
## regionnorth-east -212554.68 1495.88 -142.09 0.00
## regionwest -238367.42 1505.39 -158.34 0.00
## storeyshigh 24983.04 991.06 25.21 0.00
## month_2February 2499.78 2405.04 1.04 0.30
## month_2March 1080.56 2241.75 0.48 0.63
## month_2April 4411.63 2259.93 1.95 0.05
## month_2May -4450.62 2334.43 -1.91 0.06
## month_2June -5298.25 2289.35 -2.31 0.02
## month_2July -2286.79 2245.69 -1.02 0.31
## month_2August 2523.11 2277.43 1.11 0.27
## month_2September 6326.38 2312.60 2.74 0.01
## month_2October 5928.02 2281.24 2.60 0.01
## month_2November 10855.42 2328.24 4.66 0.00
## month_2December 11675.33 2377.92 4.91 0.00
## --------------------------------------------------------------------
##
## Estimated dispersion parameter = 8378459300
## MODEL INFO:
## Observations: 38695
## Dependent Variable: resale_price
## Type: Linear regression
##
## MODEL FIT:
## χ²(18) = 451839851213433.31, p = 0.00
## Pseudo-R² (Cragg-Uhler) = 0.58
## Pseudo-R² (McFadden) = 0.03
## AIC = 994830.76, BIC = 995002.03
##
## Standard errors: MLE
## --------------------------------------------------------------------
## Est. S.E. t val. p
## ---------------------------- ------------ --------- --------- ------
## (Intercept) 16398.79 5303.34 3.09 0.00
## floor_area_sqm 3768.31 27.78 135.67 0.00
## remaining_lease_length 3316.60 47.37 70.01 0.00
## regioneast -189269.19 1755.45 -107.82 0.00
## regionnorth -284040.91 1659.29 -171.18 0.00
## regionnorth-east -210107.51 1510.00 -139.14 0.00
## regionwest -239552.19 1519.93 -157.61 0.00
## storeyshigh 27079.19 998.56 27.12 0.00
## month_2February 2536.25 2431.84 1.04 0.30
## month_2March 1037.88 2266.74 0.46 0.65
## month_2April 4670.73 2285.13 2.04 0.04
## month_2May -4274.48 2360.45 -1.81 0.07
## month_2June -5724.85 2314.84 -2.47 0.01
## month_2July -2354.65 2270.74 -1.04 0.30
## month_2August 2158.63 2302.73 0.94 0.35
## month_2September 6198.17 2338.40 2.65 0.01
## month_2October 5804.99 2306.68 2.52 0.01
## month_2November 10084.08 2353.99 4.28 0.00
## month_2December 11342.52 2404.42 4.72 0.00
## --------------------------------------------------------------------
##
## Estimated dispersion parameter = 8566483899
## MODEL INFO:
## Observations: 38695
## Dependent Variable: resale_price
## Type: Linear regression
##
## MODEL FIT:
## χ²(9) = 458118946566707.12, p = 0.00
## Pseudo-R² (Cragg-Uhler) = 0.58
## Pseudo-R² (McFadden) = 0.03
## AIC = 994072.38, BIC = 994166.58
##
## Standard errors: MLE
## --------------------------------------------------------------------
## Est. S.E. t val. p
## ---------------------------- ------------ --------- --------- ------
## (Intercept) 194549.85 7857.41 24.76 0.00
## flat_type25-room 42785.29 1750.30 24.44 0.00
## flat_type2Executive 100957.37 3446.99 29.29 0.00
## floor_area_sqm 2097.08 63.82 32.86 0.00
## remaining_lease_length 3070.82 47.76 64.30 0.00
## regioneast -184876.16 1748.21 -105.75 0.00
## regionnorth -282901.15 1646.22 -171.85 0.00
## regionnorth-east -212547.30 1497.84 -141.90 0.00
## regionwest -238487.06 1507.36 -158.21 0.00
## storeyshigh 25033.09 992.24 25.23 0.00
## --------------------------------------------------------------------
##
## Estimated dispersion parameter = 8402177482
The r2 values and the p-values for the coefficients of each model and for each full model were compared.
The results show that the full model was sufficient in explaining the HDB resale prices across 2010 and 2022, with r2 = 0.586, F = 2740, p < 0.001. When certain variables were removed as in Models 2 and 3, the r2 values would drop to 0.577 and 0.585 respectively, indicating a drop in the explainability of the model. The full model therefore showed a moderate variability (58.6%) in explaining HDB resale prices.
While dropping the month of year variable resulted in a change in r2 of 0.001, the coefficients for the month of year variable had several values that had p-values less than 0.1 thereby still being able to explain the HDB resale prices.
Examining the Full Model further, we will visualise the simple effects to ensure our insights above are validated based on the data.
## MODEL INFO:
## Observations: 38695
## Dependent Variable: resale_price
## Type: Linear regression
##
## MODEL FIT:
## χ²(20) = 459128647515962.31, p = 0.00
## Pseudo-R² (Cragg-Uhler) = 0.59
## Pseudo-R² (McFadden) = 0.03
## AIC = 993973.99, BIC = 994162.39
##
## Standard errors: MLE
## --------------------------------------------------------------------
## Est. S.E. t val. p
## ---------------------------- ------------ --------- --------- ------
## (Intercept) 192393.54 7991.52 24.07 0.00
## flat_type25-room 42930.32 1748.21 24.56 0.00
## flat_type2Executive 101234.29 3442.75 29.41 0.00
## floor_area_sqm 2092.59 63.74 32.83 0.00
## remaining_lease_length 3070.20 47.70 64.36 0.00
## regioneast -184964.82 1745.95 -105.94 0.00
## regionnorth -282939.00 1644.10 -172.09 0.00
## regionnorth-east -212554.68 1495.88 -142.09 0.00
## regionwest -238367.42 1505.39 -158.34 0.00
## storeyshigh 24983.04 991.06 25.21 0.00
## month_2February 2499.78 2405.04 1.04 0.30
## month_2March 1080.56 2241.75 0.48 0.63
## month_2April 4411.63 2259.93 1.95 0.05
## month_2May -4450.62 2334.43 -1.91 0.06
## month_2June -5298.25 2289.35 -2.31 0.02
## month_2July -2286.79 2245.69 -1.02 0.31
## month_2August 2523.11 2277.43 1.11 0.27
## month_2September 6326.38 2312.60 2.74 0.01
## month_2October 5928.02 2281.24 2.60 0.01
## month_2November 10855.42 2328.24 4.66 0.00
## month_2December 11675.33 2377.92 4.91 0.00
## --------------------------------------------------------------------
##
## Estimated dispersion parameter = 8378459300
## MODEL INFO:
## Observations: 38695
## Dependent Variable: resale_price
## Type: Linear regression
##
## MODEL FIT:
## χ²(20) = 459128647515962.31, p = 0.00
## Pseudo-R² (Cragg-Uhler) = 0.59
## Pseudo-R² (McFadden) = 0.03
## AIC = 993973.99, BIC = 994162.39
##
## Standard errors: MLE
## --------------------------------------------------------------------
## Est. S.E. t val. p
## ---------------------------- ------------ --------- --------- ------
## (Intercept) 192393.54 7991.52 24.07 0.00
## flat_type25-room 42930.32 1748.21 24.56 0.00
## flat_type2Executive 101234.29 3442.75 29.41 0.00
## floor_area_sqm 2092.59 63.74 32.83 0.00
## remaining_lease_length 3070.20 47.70 64.36 0.00
## regioneast -184964.82 1745.95 -105.94 0.00
## regionnorth -282939.00 1644.10 -172.09 0.00
## regionnorth-east -212554.68 1495.88 -142.09 0.00
## regionwest -238367.42 1505.39 -158.34 0.00
## storeyshigh 24983.04 991.06 25.21 0.00
## month_2February 2499.78 2405.04 1.04 0.30
## month_2March 1080.56 2241.75 0.48 0.63
## month_2April 4411.63 2259.93 1.95 0.05
## month_2May -4450.62 2334.43 -1.91 0.06
## month_2June -5298.25 2289.35 -2.31 0.02
## month_2July -2286.79 2245.69 -1.02 0.31
## month_2August 2523.11 2277.43 1.11 0.27
## month_2September 6326.38 2312.60 2.74 0.01
## month_2October 5928.02 2281.24 2.60 0.01
## month_2November 10855.42 2328.24 4.66 0.00
## month_2December 11675.33 2377.92 4.91 0.00
## --------------------------------------------------------------------
##
## Estimated dispersion parameter = 8378459300
Reviewing the linear regression coefficients, we see the following insights:
- As predicted, as flat sizes increase, the resale prices would increase, all else being equal
- As predicted, as remaining lease lengths increase, resale prices would increase, all else being equal
- As predicted, for apartments on higher floors, resale prices would increase, all else being equal
Two feature variables deserve separate insights analyses:
- Region of apartment
- All else being equal, compared to apartments in the Central region, apartments in the North, East, North-East and West regions would predict lower resale prices.
- The order of resale prices by region from highest to lowest is as follows: Central > East > North-East > West > North
- Month of sale
- As predicted, resale prices would decrease in the middle of the year (May to July), increase towards the end of the year, sustain at the beginning of the year and then fall again in mid-year.
At this point, some of the more obvious predictors would be flat sizes, remaining lease lengths and apartment floor level, where it is clear that the higher they go, the higher prices would go.
It seems that key variables that are would affect my decision-making would be the region of the HDB flat and the time of year that I purchase a flat. In sum, I can expect to pay on average, the lowest prices when purchasing flats in the North and aim to purchase one in May to June to take advantage of a fall in market prices.
This is what we see when visualising mean HDB resale prices by region
across 2010 to 2022:
It is evident that resale prices are on a general upward trend, with the prices in the Central regions seeing the most volatility compared to other regions. More importantly, the prices in the North region are relatively the lowest, followed by both the West and North-East, then by East.
When visualising the month of year that a flat gets sold:
It is also evident that resale prices tend to be lowest in the mid-year months of May to June. As such, I could expect to find the lowest relative prices at this time of the year.
I set out on this project with the aim of analysing HDB resale flat prices over time to uncover any insights or trends. As I had some layman intuition about the relationship between resale prices and several factors, the analyses here validated some of those assumptions and provided insight into other factors.
In short:
- Larger flat sizes, longer remaining lease lengths and units on higher floors command higher resale prices
- Different regions command different average prices, in order from highest to lowest: Central > East > North-East > West > North
- The time of the year when a unit is sold could determine its price relative to the market, with relatively lower prices consistently seen in May to June.
While there are many other factors affecting when a couple purchases a home (e.g., necessity and urgency of moving out), the variables analysed here were readily and publicly available and could serve to inform potential homeowners with the same criteria as me when studying HDB resale flat prices. With time in hand and adequate patience, one might be able to secure a flat when the market prices them at their lowest as compared to the rest of the year and also pick units in regions where prices are lower than others.
Thank you for looking through this humble project of mine. Please feel free to reach out to me for any feedback or enquiries through my social media or email as listed here.
The code for this project can be found in the .Rmd file of the repo or also here as an R file.