6
Data Analysis: Real Estate Price Determinants
Dustin Green
9/13/2021
Word Count 873
The Determinants of Real Estate Prices
In this analysis, the real estate prices of various houses/ real estate properties in various localities in the U.S. are assessed against several perceived determinants of price. The perceived determinants of price are high schools, the color of property, state where the buyer moved from, condition of the house, size of the property in square feet, size in acres, presence of swimming pools, number of bedrooms and bathrooms, and the year in which the house was built. Using pivot tables and regression analysis, the analysis of a sample of 1000 properties was conducted using MS Excel. The results are shown in the following paragraphs. Due to the bulkiness of the selected determinants of prices, only five were selected for this analysis.
The condition of properties at the time of sale was classified as poor, good, or great. From the analysis, the poor properties prices were considerably lower than those of good and ‘great’ properties. Similarly, the analysis shows that the white and tan properties were priced considerably higher than grey and blue ones. This confirms that from experience, properties that are painted white or tan are liked more than grey and blue ones. It can be argued that sellers price properties based on the perceived market forces, including the likelihood of attracting buyers based on personal preferences and the perceived likability of features. Yu et al. (2017) make a case for the ‘product choice colors’, a concept that denotes a preference for commodities that bear certain colors across the entire market. This appears to be the concept that sellers apply to the sale and marketing of houses. It is hereby postulated that based on the preceding observation, the white and tan colors (clearly quite close) appear to paint the picture of the property that is clean and well maintained; hence the more attractive packages these properties are bound to fetch in the property market.
Based on the analysis, the ages of properties were not an influential factor in pricing. It was established that the prices varied nearly the same for properties that can be considered new and the aging ones. As shown in Figure 1 below, the levels of variation in prices across the years do not appear to follow any established or perceived pattern. This realization comes against the perception that the older the property, the lesser the income it is likely to fetch for the seller (Sun et al., 2018). However, it is imperative to borrow the argument advanced by Sun et al. (2018) regarding the lesser obvious trend where prices of some older properties do not seem to dwindle with age. The researchers note that the level of maintenance of a property is key to its attractiveness. Escalating this argument to the current data, it can be argued that the lack of distinctive variation in property prices could be borne out of careful maintenance by a considerably large number of owners. More so, renovating the property with the intention of achieving better or higher returns from it is a common practice (Sun et al., 2018). Remarkably, the data shows that some of the most expensive properties in the market are nearly a decade old, comprising some of the oldest properties included in the data. Expectedly (from the preceding discussion), properties’ age was a weak determinant of prices, accounting for only 2.67% of the total price variation.
Figure 1. Property prices across several years for selected properties.
Unlike age, the size of houses in the data had a recognizable influence on the market prices at the time of sale. As shown in Figure 2 below, the prices tended to increase gradually as the sizes increased. This feature could be attributed to the actual price of a specified land size on which a property is built. The larger the property, the more the property is bound to fetch at the property market. This argument can be illustrated as follows: considering two properties with exact similar features located side by side in a part of Texas, and assuming that one is exactly 50% larger than the other, it can be projected that the price of the larger property will be proportionately higher (by 50%) than the smaller one.
Figure 2. Property prices against the area in square feet on which they are located.
In line with the preceding argument regarding the actual land on which the building spans, the actual size of the land on which the property is located in a significant determinant of property prices. However, as shown in Figure 3 below, the actual influence of the land that is not covered by the building on pricing is considerably low. This is demonstrated by the suddenly dying trend from the data. Despite this characteristic, the property’s acreage has a considerable impact on the price, accounting for about 21% of the latter’s variation.
Figure 3. Acreage against prices.
References
Sun, T., Chand, S. & Sharpe, K. (2018). Effect of ageing on housing prices: A perspective from an overlapping generational model.
Yu, L., Westland, S., Li, Z., Pan, Q., Shin, M.J. & Won, S. (2017). The role of individual colour preferences in consumer purchase decisions.