Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Filter (ESF) approach, to more accurately account for spatial autocorrelation and enhance prediction accuracy whilst improving explainability needed for mass appraisal exercises. By undertaking such an approach, the research demonstrates the application of this method can be easily adopted for property tax jurisdictions in a framework which is more interpretable, transparent and useable within mass appraisal given its simple and appealing approach. The findings reveal that the integration of the ESFs improves model explainability, prediction accuracy and spatial residual error compared to baseline classical regression and Elastic-net regularized regression architectures, whilst offering the necessary ‘front-facing’ and flexible structure for in-sample and out-of-sample assessment needed by the assessment community for valuing the unsold housing stock. In terms of policy and practice, the study demonstrates some important considerations for mass appraisal tax assessment and for the improvement of taxation assessment and the alleviation of horizontal and vertical inequity

The X-ray spectrum of the Seyfert I galaxy Markarian 766: Dusty warm absorber or relativistic emission lines?

Competing models for broad spectral features in the soft X-ray spectrum of the Seyfert I galaxy Mrk 766 are tested against data from a 130 ks XMM-Newton observation. A model including relativistically broadened Lyalpha emission lines of O VIII N VII and C VI is a better fit to 0.3-2 keV XMM RGS data than a dusty warm absorber. Moreover, the measured depth of neutral iron absorption lines in the spectrum is inconsistent with the magnitude of the iron edge required to produce the continuum break at 17-18 Angstrom in the dusty warm absorber model. The relativistic emission line model can reproduce the broadband (0.1-12 keV) XMM EPIC data with the addition of a fourth line to represent emission from ionized iron at 6.7 keV and an excess due to reflection at energies above the iron line. The pro le of the 6.7 keV iron line is consistent with that measured for the low-energy lines. There is evidence in the RGS data, at the 3sigma level, of spectral features that vary with source flux. The covering fraction of warm absorber gas is estimated to be 12%. Iron in the warm absorber is found to be overabundant with respect to CNO, compared to solar values