UK Housing Data Analysis: Additional Price Paid Entry

I have been exploring the HM Land Registry Price Paid Data and have discovered few more things of interest.

The data contains a ‘Price Paid Data Category Type’ (this is the second last column at the time of writing this post. As per the description of the schema this field can have one of two values:

A = Standard Price Paid entry, includes single residential property sold for full market value.
B = Additional Price Paid entry including transfers under a power of sale/repossessions, buy-to-lets (where they can be identified by a Mortgage) and transfers to non-private individuals.

Therefore it seems there is a way of looking at how properties sold for full market value differ from buy-to-lets, repossessions and power of sale transactions. Proper Category B tracking only starts from October 2013.

Before we do this it is worthwhile to use the ‘Property Type’ field to filter out properties of type ‘Other’ which contribute to the overall noise because these are usually high value properties such as office buildings. The ‘Property Type’ field has the following values:

D = Detached,

S = Semi-Detached,

T = Terraced,

F = Flats/Maisonettes,

O = Other

Data Pipeline for all transactions:

Step 1: Filter out all transactions with Property Type of Other

Step 2: Group using Year and Month of Transaction

Step 3: Calculate Standard Deviations in Price, Average Price and Counts

 

Data Pipeline for Standard and Additional Price Paid Transactions (separate):

Step 1: Filter out all transactions with Property Type of Other

Step 2: Group using Price Paid Data Category Type, Year and Month of Transaction

Step 3: Calculate Standard Deviations in Price, Average Price and Counts

Tech stuff:

I used a combination of MongoDB (aggregation pipelines for standard heavy weight aggregations – such as simple grouping), Apache Spark (Java based for heavy weight custom aggregations) and Python (for creating graphs and summarising aggregated data)

Results:

In all graphs Orange points represent Category B related data, Blue represents Category A related data and Green represents a combination of both the Categories.

Transaction Counts

Price Paid Data Category A/B Transaction Count

Price Paid Data Category A/B Transaction Count

Category B transactions form a small percentage of the overall transactions (5-8% appprox.)

As the Category B data starts from October 2013 we see a rapid increase in Category B transactions which then settles to a steady rate till 2017 where we can see transactions falling as it becomes less lucrative to buy a second house to generate rental income. There is a massive variation in terms of overall and Category A transactions. But here as well we see a downward trend in 2017.

We can also see the sharp fall in transactions due to the financial crisis around 2008.

In all graphs Orange points represent Category B related data, Blue represents Category A related data and Green represents a combination of both the Categories.

Average Price

Price Paid Data Category A/B Average Price

Price Paid Data Category A/B Average Price

Here we find an interesting result. Category B prices are consistently lower than pure Category A. But given the relatively small number of Category B transactions the average price of combined transactions is fairly close to the average price of Category A transactions. This also seems to point to the fact that in case of buy to let, repossessions and power of sale conditions the price paid is below the average price for Category A. Several reasons could exist for such a result:

  1. People buy cheaper properties as buy-to-let and use more expensive properties as their main residence.
  2. Under stressful conditions (e.g. forced sale or repossession) there is urgency to sell and therefore full market rate may not be obtainable.

Standard Deviation of Prices

Price Paid Data Category A/B Price Standard Dev.

Price Paid Data Category A/B Price Standard Dev.

The variation in the price for Category B properties is quite high when compared with Category A (the standard price paid transaction). This can point to few things about the Category B market:

  1. A lot more speculative activity is carried out here therefore the impact of ‘expectation’ on price paid is very high – particularly:
    1. ‘expected rental returns’: The tendency here will be to buy cheap (i.e. lowest possible mortgage) and profit from the difference between monthly rental and mortgage payments over a long period of time.
    2. ‘expected profit from a future sale’: The tendency here will be to keep a shorter horizon and buy cheap then renovate and sell at a higher price – either through direct value add or because of natural increase in demand.
  2. For a Standard transaction (Category A) the incentive to speculate may not be present as it is a basic necessity.

Contains HM Land Registry data © Crown copyright and database right 2017. This data is licensed under the Open Government Licence v3.0.

House Price and Transactions with UK Elections

We are just getting over the not so shocking election result in UK (8th June 2017).

I wanted to look at house prices and how they are affected by election results.

The graphs below plot House Price/ Number of Transactions against date (blue dots). The data is averaged over a month and is normalised to 1.0.

The vertical lines represent UK general elections with blue representing clear results (clear majority) and black lines representing hung Parliament. There is a black line (2nd from right) that represents EU Referendum (‘Brexit’).

The orange dots represent GBP (Sterling) performing against INR (Indian Rupee) and CNY (Chinese Yuan). The data is daily average normalised to 1.0.

We can see house prices grow aggressively after clear results. The period from 2008 onward is the ‘financial’ crisis era which is further complicated by a hung Parliament in 2010. The actual recovery takes a few years and by 2014 the boom times are back! The growth is further enhanced by a Conservative majority in 2015.

It is too early to see the impact of Brexit on the housing market but as far as GBP goes there has been a fall against all major currencies.

This means investment into the UK housing market is made cheaper for ‘international’ buyers. The growth in house prices is compensated by the fall in the pound (we can see this by the relative falls in the two graphs).

Already the house price increase is cooling off (falling in many regions where they were over-inflated to begin with). With the messy general election of 2017 increasing the uncertainty, especially around Brexit, the house prices from internal demand should decrease or flatten out. We can already see this starting. People might rush in to lock their mortgage (thereby boosting short term demand) as Bank of England has indicated a rise in Interest Rates in the near future.

What happens if look at the number of transactions? The normalised graph  above shows that during the financial crisis era the transactions fell sharply. Then began to revive (correlates with the rise in house prices). The strong position of the Conservatives further supported the market.

But as soon as the Stamp Duty increase came into the picture the number of transactions started reducing and after ‘Brexit’ leading up to the 2017 General Election we can see a sharp fall in transactions.

All of these things indicate that people are not sure about what will happen in the future so are not willing to take positions of risk.

Stamp duty change

Stamp duty change (1st April 2016)

A final interesting titbit – Why is there a massive spike in transactions in a subdued period of house sales (the red arrow)? And no this is not an error! The month is March 2016 – and the spike is there because stamp duty changes were being introduced from 1st April 2016 which meant buying a second home (without selling the first one) would become a lot more expensive!

[This analysis uses the Land Registry data set which is processed using Apache Spark, Python was used to further process and plot the data]