Estimating UK House Prices using Machine Learning

House price estimation is an important subject for property owners, property developers, investors and buyers. It has featured in many academic research papers and some government and commercial reports. The price of a house may vary depending on several features including geographic location, tenure, age, type, size, market, etc. Existing studies have largely focused on applying single or multiple machine learning techniques to single or groups of datasets to identify the best performing algorithms, models and/or most important predictors, but this paper proposes a cumulative layering approach to what it describes as a Multi-feature House Price Estimation (MfHPE) framework. The MfHPE is a process-oriented, data-driven and machine learning based framework that does not just identify the best performing algorithms or features that drive the accuracy of models but also exploits a cumulative multi-feature layering approach to creating machine learning models, optimising and evaluating them so as to produce tangible insights that enable the decision-making process for stakeholders within the housing ecosystem for a more realistic estimation of house prices. Fundamentally, the MfHPE framework development leverages the Design Science Research Methodology (DSRM) and HM Land Registry’s Price Paid Data is ingested as the base transactions data. 1.1 million London-based transaction records between January 2011 and December 2020 have been exploited for model design, optimisation and evaluation, while 84,051 2021 transactions have been used for model validation. With the capacity for updates to existing datasets and the introduction of new datasets and algorithms, the proposed framework has also leveraged a range of neighbourhood and macroeconomic features including the location of rail stations, supermarkets, bus stops, inflation rate, GDP, employment rate, Consumer Price Index (CPIH) and unemployment rate to explore their impact on the estimation of house prices and their influence on the behaviours of machine learning algorithms. Five machine learning algorithms have been exploited and three evaluation metrics have been used. Results show that the layered introduction of new variety of features in multiple tiers led to improved performance in 50% of models, a change in the best performing models as new variety of features are introduced, and that the choice of evaluation metrics should not just be based on technical problem types but on three components: (i) critical business objectives or project goals; (ii) variety of features; and (iii) machine learning algorithms

Nitrogen effect on zinc biofortification of maize and cowpea in Zimbabwean smallholder farms

Agronomic biofortification of crops with zinc (Zn) can be enhanced under increased nitrogen (N) supply. Here, the effects of N fertilizer on grain Zn concentration of maize (Zea mays L.) and cowpea (Vigna unguiculata L.) were determined at two contrasting sites in Zimbabwe over two seasons. All treatments received soil and foliar zinc‐sulphate fertilizer. Seven N treatments, with three N rates (0, 45, and 90 kg ha−1 for maize; 0, 15, and 30 kg ha−1 for cowpea), two N forms (mineral and organic), and combinations thereof were used for each crop in a randomized complete block design (n = 4). Maize grain Zn concentrations increased from 27.2 to 39.3 mg kg−1 across sites. At 45 kg N ha−1, mineral N fertilizer increased maize grain Zn concentration more than organic N from cattle manure or a combination of mineral and organic N fertilizers. At 90 kg N ha−1, the three N fertilizer application strategies had similar effects on maize grain Zn concentration. Co‐application of N and Zn fertilizer was more effective at increasing Zn concentration in maize grain than Zn fertilizer alone. Increases in cowpea grain Zn concentration were less consistent, although grain Zn concentration increased from 39.8 to 52.7 mg kg−1 under optimal co‐applications of N and Zn. Future cost/benefit analyses of agronomic biofortification need to include information on benefits of agro‐fortified grain, complex farmer management decisions (including cost and access to both N and Zn fertilizers), as well as understanding of the spatial and site‐specific variation in fertilizer responses

Genome-wide identification of the Phaseolus vulgaris sRNAome using small RNA and degradome sequencing

Background: MiRNAs and phasiRNAs are negative regulators of gene expression. These small RNAs have been extensively studied in plant model species but only 10 mature microRNAs are present in miRBase version 21, the most used miRNA database, and no phasiRNAs have been identified for the model legume Phaseolus vulgaris. Thanks to the recent availability of the first version of the common bean genome, degradome data and small RNA libraries, we are able to present here a catalog of the microRNAs and phasiRNAs for this organism and, particularly, we suggest new protagonists in the symbiotic nodulation events.Results: We identified a set of 185 mature miRNAs, including 121 previously unpublished sequences, encoded by 307 precursors and distributed in 98 families. Degradome data allowed us to identify a total of 181 targets for these miRNAs. We reveal two regulatory networks involving conserved miRNAs: those known to play crucial roles in the establishment of nodules, and novel miRNAs present only in common bean, suggesting a specific role for these sequences. In addition, we identified 125 loci that potentially produce phased small RNAs, with 47 of them having all the characteristics of being triggered by a total of 31 miRNAs, including 14 new miRNAs identified in this study.Conclusions: We provide here a set of new small RNAs that contribute to the broader knowledge of the sRNAome of Phaseolus vulgaris. Thanks to the identification of the miRNA targets from degradome analysis and the construction of regulatory networks between the mature microRNAs, we present here the probable functional regulation associated with the sRNAome and, particularly, in N2-fixing symbiotic nodules.Peer reviewedBiochemistry and Molecular Biolog

A Machine Learning Framework for House Price Estimation

House prices estimation has been the focus of both commercial and academic researches with various approaches being explored. Depending on the location, size, age, time and other factors, the value of houses may vary. This paper presents a modularized, process-oriented, data-enabled and machine learning-based framework, designed to help the decision-makers within the housing ecosystem to have a more realistic estimation of the house prices. The development of the framework leverages the Design Science Research Methodology (DSRM) and the HM Land Registry Price Paid Data is ingested into the framework as the base transactions data. 1.1 million London based transaction records between January 2011 and December 2020 have been exploited for model design and evaluation. The proposed framework also leverages a range of neighborhood data including the location of rail stations, supermarkets and bus stops to explore the possible impact on house prices. Five machine learning algorithms have been exploited and three evaluation metrics have been presented and with a focus on RMSE. Results show that an increase in the variety of parameters enables improved accuracy which ultimately will enable decision making. The potential for future work based on this paper can explore the impact of the introduction of other groups of data on the accuracy of machine learning models designed for the estimation of house prices

Effect of Postemergence, Supplemental Inoculation on Nodulation and Symbiotic Performance of Soybean (Glycine max (L.) Merrill) at Three Levels of Soil Nitrogen

The influence of a supplementary bradyrhizobial inoculation after an initial seed slurry inoculation with the same strain on nodulation and N(2) fixation in soybeans was examined in the greenhouse. The plants were grown in a Typic Eutrocrepts soil: sand mixture containing 25, 65, or 83 mg of N per kg (i.e., native soil N plus (15)N-labeled ammonium sulfate). Harvests were made at early flowering and physiological maturity. The supplementary inoculations which were made 14 or 21 days after planting (DAP) caused formation of substantially more nodules than the single slurry inoculation did. Autoregulation was therefore not completely successful in preventing subsequent infections. For the slurry-inoculated plants, at both harvests the proportion of N derived from fixation was greatest in the soil containing the least N, and only slight increases in N(2) fixation resulted from a second inoculation. The inhibition of N(2) fixation at the higher N levels was significantly reduced by a second inoculation at 21 DAP; this treatment resulted in at least a doubling of both the percentage and total amount of N(2) fixed by the single slurry inoculation at physiological maturity. The N(2) fixation increases resulting from the supplementary inoculation at 14 DAP were less pronounced and not significant. Greater N(2) fixation was frequently not reflected by increased total N or dry matter yield, suggesting that the major benefit of the increased fixation was a decreased dependence of plants on soil N for growth