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+---
+title: House prices and Vacant homes
+section: data
+colour: c4-bg
+desc:
+url: /data/house-prices-vacant-homes/
+---
+### Introduction
+
+We want to explore the relationship between vacant homes and house prices, to understand what effect vacant homes have on the housing market and how this has changed over time.
+
+Simplistically, we could plot the number of vacant homes against the average house price for lots of places in England. But this wouldn't account for inflation or regional population and housing differences.
+
+To account for inflation, we'll use the [median house price to residence based earnings ratio](https://www.ons.gov.uk/peoplepopulationandcommunity/housing/datasets/ratioofhousepricetoresidencebasedearningslowerquartileandmedian). This is published by the Office for National Statistics (ONS) and is available at local authority level. Residence based earnings, for a given local authority, are counted based on where people live, rather than their place of work (which could be in a different local authority).
+
+To account for regional population/housing differences, we will use the number of vacant homes per 1000 dwellings. This is calculated by dividing the respective counts in [Table 615: vacant dwellings by local authority district: England, from 2004](https://www.gov.uk/government/statistical-data-sets/live-tables-on-dwelling-stock-including-vacants) by the dwelling count estimates in [Table 125: dwelling stock estimates by local authority district](https://www.gov.uk/government/statistical-data-sets/live-tables-on-dwelling-stock-including-vacants) and multiplying by 1000. This gives a relative number of vacant homes that is porportional to the total number of dwellings in a given local authority.
+
+The aim is to make a scatter plot of house price to earnings ratio against vacant homes per 1000 dwellings. Each point on the plot will be a single local authority, and we'll make 3 of these plots for 2004 (the earliest common year in both datasets), 2014, and 2023 (the most recent year in both datasets).
+
+### Method
+
+First, we will import all the modules we're going to need. We also need to set up our file paths so that anyone who cloned this repo could run this notebook.
+
+
+```python
+from pathlib import Path
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+ROOT = Path('../')
+ROOT.resolve()
+```
+
+
+
+
+ PosixPath('/Users/lukestrange/Code/housing')
+
+
+
+Read in the data we need. We processed this already so it will be straightforward to combine it into one table.
+
+
+```python
+hp_wage_ratio = pd.read_parquet(ROOT / 'data/house-prices/site/hp_to_wage_ratio.parquet')
+vh = pd.read_csv(ROOT / 'data/vacant-homes/standard/AllCombined_Cleaned_2024.csv')
+```
+
+
+```python
+# Convert date to int type
+hp_wage_ratio['date'] = hp_wage_ratio['date'].astype(int)
+
+# Add a more useful column name
+hp_wage_ratio.rename(columns={'value':'wage_ratio'}, inplace=True)
+```
+
+Vacant homes and dwellings data are stored in the same file. We'll use a copy of `vh` to filter these out respectively.
+
+
+```python
+vacant_homes = vh.copy()
+vacant_homes = vacant_homes[vacant_homes['Measure'] == 'AllVacants']
+vacant_homes['date'] = vacant_homes['date'].astype(int)
+vacant_homes.rename(columns={'Value': 'AllVacants'}, inplace=True)
+```
+
+
+```python
+dwellings = vh.copy()
+dwellings = dwellings[dwellings['Measure'] == 'Dwellings']
+dwellings['date'] = dwellings['date'].astype(int)
+dwellings.rename(columns={'Value': 'Dwellings'}, inplace=True)
+```
+
+
+```python
+# Merge all three dataframes into one.
+combined = vacant_homes.merge(hp_wage_ratio, on=['geography_code', 'date'], how='inner').merge(dwellings, on=['geography_code', 'date'], how='inner')
+
+# Calculate the number of vacant homes per 1000 dwellings
+combined['vacants_per_thousand_dwellings'] = (1000 * combined['AllVacants'] / combined['Dwellings']).round()
+
+# Only include Local authority districts. No aggregates like regions, counties etc.
+combined = combined[combined.geography_code.str.startswith(("E06", "E07", "E08", "E09"))]
+
+# Add a column that says whether or not the local authority is a London Borough (E09 codes).
+combined['London borough'] = combined.geography_code.str.startswith('E09')
+```
+
+A quick note here to say that in the final line above, we exlcude anything that isn't a local authority district (so no counties, regions etc.). This is so that there are no aggregations of the data that is already being displayed.
+
+Below, we will import some modules and write some code to plot the data on a scatterplot. We'll write this as a function since we will re-use it for individual year's data.
+
+
+```python
+# Set the font to Arial
+plt.rcParams["font.sans-serif"] = ["Arial"]
+
+def make_scatterplot(data, year):
+ # Filter the data by year
+ single_year = data[data.date == year]
+ # Make the scatterplot
+ ax = sns.scatterplot(data=single_year, x='vacants_per_thousand_dwellings', y='wage_ratio', hue='London borough')
+ # Set some params
+ ax.set(xlabel='Vacant Homes per 1000 Dwellings', ylabel='House price to residence-based earnings ratio', title=str(year), xlim=(0, 100), ylim=(0, 25))
+ # Rename the legend
+ ax.legend(title='Location')
+ # Rename the legend items to be more informative
+ handles, previous_labels = ax.get_legend_handles_labels()
+ ax.legend(handles=handles, labels=['Not London Boroughs (E06, E07, E08)', 'London Boroughs (E09)'])
+ # Save the figure
+ plt.savefig(ROOT / f"src/assets/images/vacant_homes_house_prices_to_wage_ratio/{year}.png")
+ # Show the figure
+ plt.show()
+ return
+```
+
+
+```python
+make_scatterplot(combined, 2004)
+make_scatterplot(combined, 2014)
+make_scatterplot(combined, 2023)
+```
+
+
+
+ 
+
+ 
+
+ 
+
+