Analysis of NYC Property Sales in 2020
title: “NYC property sales in 2020” author: “Tahsin Ifnoor Sayeed” date: “2023-08-17” output: html_document ———————-
Section 1:
A summary statistics table for property sale prices in each borough
Importing the data
nyc2020 <- read_csv("nyc2020.csv")
head(nyc2020, n=10)## # A tibble: 10 × 21
## borough neighborhood building_class_category tax_class_at_present block lot
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 2 BATHGATE 01 ONE FAMILY DWELLINGS 1 3029 36
## 2 2 BATHGATE 01 ONE FAMILY DWELLINGS 1 3030 65
## 3 2 BATHGATE 01 ONE FAMILY DWELLINGS 1 3030 67
## 4 2 BATHGATE 01 ONE FAMILY DWELLINGS 1 3030 67
## 5 2 BATHGATE 01 ONE FAMILY DWELLINGS 1 3039 29
## 6 2 BATHGATE 01 ONE FAMILY DWELLINGS 1 3045 12
## 7 2 BATHGATE 01 ONE FAMILY DWELLINGS 1 3046 52
## 8 2 BATHGATE 02 TWO FAMILY DWELLINGS 1 2912 118
## 9 2 BATHGATE 02 TWO FAMILY DWELLINGS 1 2912 130
## 10 2 BATHGATE 02 TWO FAMILY DWELLINGS 1 2927 131
## # ℹ 15 more variables: ease_ment <lgl>, building_class_at_present <chr>,
## # address <chr>, apartment_number <chr>, zip_code <dbl>,
## # residential_units <dbl>, commercial_units <dbl>, total_units <dbl>,
## # land_square_feet <dbl>, gross_square_feet <dbl>, year_built <dbl>,
## # tax_class_at_time_of_sale <dbl>, building_class_at_time_of_sale <chr>,
## # sale_price <dbl>, sale_date <chr>A table of summary statistics for all NYC boroughs:
df_manhattan <- nyc2020 %>%
filter(borough==1) %>%
group_by(borough) %>%
summarise(n(),mean_price = mean(sale_price),
median_price = median((sale_price)),
max_price = max(sale_price),
min_price = min(sale_price)
)
df_bronx <- nyc2020 %>%
filter(borough==2) %>%
group_by(borough) %>%
summarise(n(), mean_price = mean(sale_price),
median_price = median((sale_price)),
max_price = max(sale_price),
min_price = min(sale_price)
)
df_brooklyn <- nyc2020 %>%
filter(borough==3) %>%
group_by(borough) %>%
summarise(n(),mean_price = mean(sale_price),
median_price = median((sale_price)),
max_price = max(sale_price),
min_price = min(sale_price)
)
df_queens <- nyc2020 %>%
filter(borough==4) %>%
group_by(borough) %>%
summarise(n(),mean_price = mean(sale_price),
median_price = median((sale_price)),
max_price = max(sale_price),
min_price = min(sale_price)
)
df_statenisl <- nyc2020 %>%
filter(borough==5) %>%
group_by(borough) %>%
summarise(n(), mean_price = mean(sale_price),
median_price = median((sale_price)),
max_price = max(sale_price),
min_price = min(sale_price)
)
all_boroughs <- rbind(df_brooklyn, df_manhattan, df_bronx, df_queens, df_statenisl)
all_boroughs %>% arrange(borough)## # A tibble: 5 × 6
## borough `n()` mean_price median_price max_price min_price
## <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 12880 3285516. 775000 978090439 0
## 2 2 6645 1575194. 300000 87400000 0
## 3 3 19899 944205. 405600 137750000 0
## 4 4 21701 687600. 332000 369300000 0
## 5 5 7559 460579. 425000 85000000 0- As evident in the table above, the greatest number of sales records came from Queens Borough, closely followed by Brookly Borough, in 2020. Interestingly, there was at least one property that sold for zero price in every borough.
- Mean Prices
- Properties, on average, sold for highest prices in Manhattan and Bronx.
- Lowest mean price is seen in Staten Island
- Median Prices
- Median prices reveal a different story altogether!
- Drastic outliers are evident in the sale prices from almost all boroughs expect Staten Island. These outliers seem to have inflated the mean prices unduly.
- In terms of median , although Manhattan still appears to have
garnered the highest prices, Staten Island now takes the 2nd spot
followed by Brroklyn, Queens and Bronx.
- Max Prices
- The deduction on the existence of outliers is further vindicated by maximum prices!
- Greatest outliers are seen in Manhattan, Queens and Brooklyn.
Section 2:
A histogram showing the sale price distribution in NYC and each borough
val_lab(nyc2020$borough) <- make_labels("1 Manhattan
2 Bronx
3 Brooklyn
4 Queens
5 Staten Island")
val_lab(nyc2020$borough)## Manhattan Bronx Brooklyn Queens Staten Island
## 1 2 3 4 5ggplot(nyc2020, aes(x=sale_price)) +
geom_histogram(bins=50) +
scale_x_log10() +
facet_wrap(~borough) +
theme_clean() +
labs(x="Sale Price", y="No. of Transactions", title="Distribution of Sale Price in NYC Boroughs")
Section 3:
A bar chart showing the number of transactions in each borough
by_borough_trans <- nyc2020 %>%
group_by(borough) %>%
summarize(trans = n())
val_lab(by_borough_trans$borough) <- make_labels("1 Manhattan
2 Bronx
3 Brooklyn
4 Queens
5 Staten Island")
ggplot(by_borough_trans, aes(x= reorder(borough, +trans), y=trans)) +
geom_col(fill="#1b98e0") +
labs(title="Transactions in each NYC Boroughs", x="Boroughs", y="Number of Transactions") +
theme_clean()
Section 4:
A scatter plot showing the relationship between building age of a property and sale price
nyc_build_100 <- nyc2020 %>%
mutate(building_age = (2020 - year_built)) %>%
filter(building_age <100)
ggplot(nyc_build_100, aes(x=building_age, y=sale_price, alpha=1/10)) +
geom_point() +
geom_smooth() +
scale_y_log10() +
labs(x= "Building Age", y="Sale Price", title="Is Sale Price Related to Building Age?") +
theme_clean()
Section 5:
A line chart showing property sale price in Manhattan from 01/01/2020 to 31/12/2020
class(nyc2020$sale_date)## [1] "character"# install.packages("lubridate")
nyc2020$sale_date2 = mdy(nyc2020$sale_date)
class(nyc2020$sale_date2)## [1] "Date"daily_mean_price <- nyc2020 %>%
filter(borough == 1, sale_date2 >= '2020-01-01' & sale_date2 <= '2020-12-31') %>%
group_by(sale_date2) %>%
summarize(daily_mean = mean(sale_price)) %>%
arrange(sale_date2)
ggplot(daily_mean_price, aes(x=sale_date2, y=daily_mean)) +
geom_line(color="#1b98e0") +
scale_y_log10() +
labs(x= "Date", y="Daily Mean Sale Price", title="Daily Average Property Sale Prices in Manhattan") +
theme_clean()