House prices in 'SG12 9', Ware

This article reveals price per square metre data and various charts to help you understand current housing market in 'SG12 9' (Ware) - statistics were last calculated on 03 December 2024.

Defining 'SG12 9'

This analysis is limited to properties whose postcode starts with "SG12 9", this is also called the postcode sector. There are no official postcode sector names so I've just labelled it SG12 9, Ware. It is shown in red on the map below.

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You can click on the map above to change to a neighbouring sector, or you can use the search form below.

Price per square metre

Knowing the average house price in SG12 9 is not much use. However, knowing average price per square metre can be quite useful. Price per sqm allows some comparison between properties of different size. We define price per square metre as the sold price divided by the internal area of a property:

£ per sqm = price ÷ internal area

For example in October 2024, 21, Loxley Court, Ware, SG12 9FF sold for £290,000. Given the internal area of 69 square metres recorded on the EPC, the price per sqm is £290,000 ÷ 69 sqm = £4,202.

England & Wales have been officially metric since 1965. However house price per square foot is prefered by some estate agents and those of sufficiently advanced age ;-). It is a huge pain to code the automatic conversion for square meters to square feet for all the graphs and charts on SG12 9 and elsewhere. All the conditionals turn my tidy code for into spaghetti. I will get around to it at some point, but for now you can just divide everything by 10 in your head, move a decimal place and you'll be close enough. If you want to be more precise 1 sqm = 10.76391 sqft.

Distribution of £ per sqm for 'SG12 9' vs 'SG12'

The chart above is called a histogram, it helps you see the distribution of house price per sqm in SG12 9 To make this chart we put all the sales data into a series of £ per sqm 'buckets' (e.g. £4,700 to £5,000, £5,000 to £5,300, £5,300 to £5,600 etc...) we then count the number of sales with within in each bucket and plot the results. The histogram is based on 155 sales that took place in SG12 9, Ware in the last 24 months.

Generate a custom histogram like the one above but based on your own criteria.

You can see the spread of prices above. This is because although internal area is a key factor in determining valuation, it is not the only factor. Many factors other than size affect desirability; these factors could be condition, aspect, garden size, negotiating power of the vendor etc.

The spread of prices will give you a feel of the typical range to expect in SG12 9, Ware. Notably, only 25% of properties that sold recently were valued at more than £5,710 sqm. For anything to be valued more than this means it has to be more desireable than the clear majority of SG12 9 homes.

Box plot of £ per sqm for SG12 9

Tip: click on the chart to see the values.

The chart above is called a boxplot (or a box-and-whisker plot). Box plots, like histograms, are used to graphically represent the distribution of data, showing the central tendency, spread of the distribution. In the context of £ per square metre property price distributions, box plots represent the variation in property prices within a geographic area e.g. Ware. The chart above shows a boxplot for 'SG12 9' as well as the 'SG12' postcode district.

  • Median: The horizontal line inside the box represents the median (£ per square meter). This is the midpoint of the data, meaning 50% of the prices are below this value, and 50% are above. The middle price per square metre in 'SG12 9' is £4,860.
  • Interquartile Range (IQR): The box spans from the 25th percentile (Q1) to the 75th percentile (Q3). This is the range where the middle 50% of the data lies, giving a good indication of the typical price spread. Of the 155 transactions in SG12 9, Ware half were sold for between £4,080 and £5,710 per square metre.
  • Whiskers: In our case, the whiskers extend from the 9th percentile (at the lower end) to the 91st percentile (at the upper end), This provides a slightly broader view of the distribution by including the middle 82% of records. The whiskers capture most of the variation but exclude extreme outliers caused by data errors in recording sold house prices or internal area.
  • 'n=' is the number of property transactions the box plot is based on; 155 for SG12 9, Ware.

    • Property price map for Ware

    Have a look at the interactive price map I created for myself. Use it to explore 'SG12 9' house prices all the way down to individual property plots.

    Property price heatmap for Ware
    House price map for Ware

    Ware house price forecasting

    I cannot tell what house prices will do in the future and don't believe anyone who says they can. However we can plot price trends, I have done this in the chart below for SG12 9 (Ware) compared with the wider postcode district of SG12. You can extrapolate from this based on your own views on future interest rates, inflation and other factors.

    House price index for SG12 9

    Tip: click on the legend items to show/hide different lines

    Download house price index as CSV (premium users only).

    The chart above shows changes in 'SG12 9' property prices over the last 20 years. The index is calculated from the average price paid per sqm for property in SG12 9 and is set to 100 in 2004. I'm comparing the trends for SG12 9,Ware with the wider postcode district of SG12 What is more interesting is to look at the difference between flats and houses, even those in the same area follow a very different trend, to get a robust enough sample size to see this we need to zoom out and look at house price trends for the entire East Hertfordshire local authority.

    The dashed lines show nominal house price changes, the solid lines show the same data adjusted for inflation. Economists call this the 'real' price change. You have to take inflation into account when comparing prices over time. It's calculated using the formula:

    Real Rate of Return = (1 + Nominal Rate) ÷ (1 + Inflation Rate) – 1
    In this formula, the nominal rate is the rate of change before any adjustments, and the inflation rate is taken from the Consumer Price Index. The real rate of return is a more accurate measure of change in value, because £1 today does not have the same buying power as £1 in the past. For example, if a savings account pays an interest rate of 3% per year and the inflation rate is 5% per year, the real rate of return is -2%. This means that the investment's value is shrinking by 2% each year.

    Historic returns for SG12 9
    SG12 9 sector SG12 district
    Nominal Real Nominal Real
    20 yr per annum 2.8% 0.1% 3.1% 0.4%
    20 yr total 72.1% 1.9% 83.4% 8.6%
    10 yr per annum 1.9% -0.8% 3.2% 0.4%
    10 yr total 21.0% -8.1% 37.6% 4.5%
    5 yr per annum -0.0% -3.9% 1.3% -2.6%
    5 yr total -0.1% -18.2% 6.8% -12.6%
    1 yr per annum -7.7% -11.4% -3.7% -7.6%
    1 yr total -7.7% -11.4% -3.7% -7.6%

    This table complements the house price index chart above, presenting the data in a more detailed format. It breaks down the information into 20-year, 10-year, 5-year, and 1-year periods, further categorized by property type. For each period, we display both a per annum rate of change and a total rate of change.

    The total rate of change represents the overall change over the entire period. The formula for total return is:

    Total return = (Index at end of period ÷ Index at start of period) - 1

    The per annum rate of change is the annualized rate of change over the period. This is equivalent to the annual bank savings rate you would need to achieve the same total return over the given period. This annualized return is also known as the Compound Annual Growth Rate (CAGR). The formula for CAGR is:

    CAGR = (1 + Total return) ^ (1 ÷ Number of years) - 1

    Some specific examples:

    • Over the past 20 years, SG12 9 sector have seen a 0.1% annual change when adjusted for inflation. This translates to a total change of 1.9% in real terms.
    • Over the past 5 years, SG12 district have seen a -2.6% annual change when adjusted for inflation. This translates to a total change of -12.6% in real terms.

    Most recent SG12 9 sales

    For the most recent sales activity, rather than a summarized average, it is better to see the underlying data. This is shown in the chart below, where blue dots represent individual sales, click on them to see details. If there is an obvious trend you should be able to spot it here amid the noise from outliers.

    Tip: hover over dots to see details

    Street level data

    Street Avg size Avg £sqm Recent sales
    Station Road, Ware, SG12 9U 55 sqm £5,041 23
    Broadmeads, Ware, SG12 9E 63 sqm £3,969 18
    Broadmeads, Ware, SG12 9H 64 sqm £4,058 16
    Wickhams Wharf, Ware, SG12 9P 61 sqm £4,693 16
    Millacres, Ware, SG12 9P 68 sqm £4,216 16
    Crib Street, Ware, SG12 9H 58 sqm £4,785 15
    Willow View, Ware, SG12 9F 71 sqm £4,039 12
    Church Street, Ware, SG12 9E 61 sqm £4,992 12

    Search for your street here.

    Nearby geographies

    The table below shows how 'SG12 9' compares to the other postcode sectors in SG12.

    Sector Lower quartile Median Upper quartile Sales in last 2yr
    SG13 7 Hertford £4,680 sqm £5,370 sqm £6,140 sqm 257
    SG12 9 Ware £4,080 sqm £4,860 sqm £5,710 sqm 155
    SG12 8 Stanstead Abbotts £4,420 sqm £5,110 sqm £5,830 sqm 119
    SG12 7 Ware £4,590 sqm £5,310 sqm £5,970 sqm 183
    SG12 0 Ware £4,460 sqm £5,080 sqm £5,970 sqm 203
    EN11 9 Hoddesdon £4,260 sqm £4,780 sqm £5,330 sqm 156

    Raw data

    Our analysis of SG12 9 is derived from what is essentially a big table of sold prices from Land Registry with added property size information. Below are three rows from this table to give you an idea.

    Address Paid sqm £/sqm
    21, Loxley Court, Ware £290,000
    Oct-2024
    		 69 4,202
    7, Maltings Court, Ware £315,000
    Oct-2024
    		 66 4,772
    24, Dixons Court, Ware £265,000
    Oct-2024
    		 66 4,015

    See the entire list of all sales in SG12 9 here.

    About

    I created HouseMetric because I wanted to see this data and analysis myself, I also wanted to teach myself to build a website. Please give me feedback or spread the word about it. I'm constantly tinkering and adding more stuff to it.